Replication code for “Crossing the Linguistic Causeway: Ethno-national Differences on Soundscape Attributes in Bahasa Melayu”
R version 4.1.1 (2021-08-10)
Platform: aarch64-apple-darwin20 (64-bit)
locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: CircE(v.1.1), circumplex(v.0.3.8), RTHORR(v.0.1.2), gdata(v.2.18.0), ggthemes(v.4.2.4), ggExtra(v.0.10.0), ggforce(v.0.4.1), factoextra(v.1.0.7), kableExtra(v.1.3.4), ggbrace(v.0.1.0), ggsignif(v.0.6.3), muStat(v.1.7.0), fmsb(v.0.7.1), conover.test(v.1.1.5), rstatix(v.0.7.0), psych(v.2.1.6), ggfortify(v.0.4.14), readxl(v.1.4.2), reshape2(v.1.4.4), plyr(v.1.8.7), janitor(v.2.1.0), lubridate(v.1.9.2), forcats(v.1.0.0), stringr(v.1.5.0), dplyr(v.1.1.1), purrr(v.1.0.1), readr(v.2.1.4), tidyr(v.1.3.0), tibble(v.3.2.1), ggplot2(v.3.4.2), tidyverse(v.2.0.0), dataverse(v.0.3.10) and pander(v.0.6.5)
loaded via a namespace (and not attached): colorspace(v.2.0-3), ellipsis(v.0.3.2), rio(v.0.5.27), snakecase(v.0.11.0), rstudioapi(v.0.14), farver(v.2.1.1), ggrepel(v.0.9.1), fansi(v.1.0.3), xml2(v.1.3.4), mnormt(v.2.0.2), knitr(v.1.39), polyclip(v.1.10-0), jsonlite(v.1.8.4), broom(v.1.0.4), shiny(v.1.7.4), compiler(v.4.1.1), httr(v.1.4.5), backports(v.1.4.1), assertthat(v.0.2.1), fastmap(v.1.1.0), cli(v.3.6.1), later(v.1.3.0), tweenr(v.1.0.2), htmltools(v.0.5.5), tools(v.4.1.1), gtable(v.0.3.0), glue(v.1.6.2), Rcpp(v.1.0.9), carData(v.3.0-4), cellranger(v.1.1.0), vctrs(v.0.6.3), svglite(v.2.1.0), nlme(v.3.1-152), xfun(v.0.31), openxlsx(v.4.2.4), rvest(v.1.0.3), timechange(v.0.2.0), mime(v.0.12), miniUI(v.0.1.1.1), lifecycle(v.1.0.3), gtools(v.3.9.2), MASS(v.7.3-54), scales(v.1.2.0), hms(v.1.1.3), promises(v.1.2.0.1), parallel(v.4.1.1), yaml(v.2.3.5), curl(v.4.3.2), gridExtra(v.2.3), stringi(v.1.7.8), permute(v.0.9-7), zip(v.2.2.0), rlang(v.1.1.1), pkgconfig(v.2.0.3), systemfonts(v.1.0.4), evaluate(v.0.15), lattice(v.0.20-44), tidyselect(v.1.2.0), magrittr(v.2.0.3), R6(v.2.5.1), generics(v.0.1.2), DBI(v.1.1.1), pillar(v.1.9.0), haven(v.2.5.2), foreign(v.0.8-81), withr(v.2.5.0), abind(v.1.4-5), car(v.3.0-11), utf8(v.1.2.2), tmvnsim(v.1.0-2), tzdb(v.0.3.0), rmarkdown(v.2.14), grid(v.4.1.1), data.table(v.1.14.8), digest(v.0.6.29), webshot(v.0.5.3), xtable(v.1.8-4), httpuv(v.1.6.5), munsell(v.0.5.0) and viridisLite(v.0.4.0)
The survey data was collected via a Matlab GUI. The survey and demographic data are stored in a public data repository at https://doi.org/10.21979/N9/9AZ21T.
Load supplementary data for analysis
- SATP zsm Stage 1: https://doi.org/10.21979/N9/0NE37R
- ARAUS dataset: https://doi.org/10.21979/N9/9OTEVX
#Dataverse dataset doi links
data.satp.zsm2.name = "10.21979/N9/9AZ21T" #dataset linked to this paper
data.satp.zsm1.name = "10.21979/N9/0NE37R" #satp stage 1 dataset
data.araus.name = "10.21979/N9/9OTEVX" #araus dataset
# Loading SATP Stage 2 dataset
## Define a list of data frame names and associated dataset file names
data.names <- data.frame(
df.name=c("data.subj.zsm2", "data.demo.zsm2", #zsm2
"data.main.zsm1","data.der.zsm1" #zsm1
),
filename=c("SATP_Stage2_zsm_questionnaire.tab",#zsm2 demographic
"SATP_Stage2_zsm_demographics.tab", #zsm2 demographic
"SATP_Stage1_zsm_main.tab", #zsm1 main-axis attributes
"SATP_Stage1_zsm_derived.tab" #zsm1 derived-axis attributes
))
## Load datasets into a list
data.satp.zsm2.l <- datavLoader(data.names[1:2,], data.satp.zsm2.name)[1] "Loading: data.subj.zsm2; From: 10.21979/N9/9AZ21T"
[1] "Loading: data.demo.zsm2; From: 10.21979/N9/9AZ21T"
# Loading SATP Stage 1 dataset
data.satp.zsm1.l <- datavLoader(data.names[3:4,], data.satp.zsm1.name)[1] "Loading: data.main.zsm1; From: 10.21979/N9/0NE37R"
[1] "Loading: data.der.zsm1; From: 10.21979/N9/0NE37R"
data.araus.filename <- "data.zip" #filename of araus data
#download data.zip
data.araus.bin<-dataverse::get_file_by_name(filename = data.araus.filename,
dataset = data.araus.name)
#write the binary file to zip
writeBin(data.araus.bin, paste0("./data/",data.araus.filename))
#unzip and retrieve only responses.csv and participants.csv
unzip(data.araus.filename,
files=c("data/responses.csv","data/participants.csv"))Warning in unzip(data.araus.filename, files = c("data/responses.csv",
"data/participants.csv")): error 1 in extracting from zip file
#araus participant data
data.araus.participant <- read_csv("./data/participants.csv") %>%
dplyr::filter(ethnic==2 & residence_length==1) %>% #ethnic malays
dplyr::select(participant)
#subjective test data
data.araus<-read_csv("./data/responses.csv") %>%
#only local resident + ethnic malays
dplyr::filter(participant %in% data.araus.participant$participant) %>%
#only soundscapes; no augmentation
dplyr::filter(grepl("silence",masker)) %>%
#remove test and calibration folds
dplyr::filter(!fold_r %in% c(0,-1)) %>%
#compute ISOPL and ISOEV
dplyr::mutate(ISOPL=((pleasant-annoying)+
cospi(0.25)*(calm-chaotic)+
cospi(0.25)*(vibrant-monotonous))/
(4+sqrt(32))) %>%
dplyr::mutate(ISOEV=((eventful-uneventful)+
cospi(0.25)*(chaotic-calm)+
cospi(0.25)*(vibrant-monotonous))/
(4+sqrt(32))) %>%
#select only relevant columns
dplyr::select(c(participant,soundscape,
eventful,vibrant,pleasant,calm,
uneventful, monotonous, annoying, chaotic,
ISOPL, ISOEV))#no of participants
n.participsnts.SG <- length(
unique(data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="SG") %>%
.$participantID))
n.participsnts.MY.M <- length(
unique(data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="MY:M") %>%
.$participantID))
n.participsnts.MY.O <- length(
unique(data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="MY:O") %>%
.$participantID))
#summarise language fluency count by groups
data.demo.merged.gender.fluency <- data.satp.zsm2.l$data.demo.zsm2 %>%
dplyr::mutate(fluency=ifelse(
set=="UPM", #fluent in oral zsm >6
ifelse(as.numeric(as.character(fluency))>6,"Yes","No"),
"Yes")) %>%
group_by(group,fluency) %>%
dplyr::summarise(count=n()) %>%
pivot_wider(names_from = group,values_from = c(count)) %>%
column_to_rownames(var = "fluency") %>%
mutate_all(~replace(., is.na(.), 0)) %>%
rbind(data.satp.zsm2.l$data.demo.zsm2 %>%
group_by(group,gender) %>%
dplyr::summarise(count=n()) %>%
pivot_wider(names_from = group,values_from = c(count)) %>%
column_to_rownames(var = "gender"))Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `fluency = ifelse(...)`.
Caused by warning in `ifelse()`:
! NAs introduced by coercion
#demos stats for age, written & spoken fluency scores
data.demo.merged.numeric <- data.satp.zsm2.l$data.demo.zsm2 %>%
dplyr::mutate(fluency=ifelse(
fluency=="Yes",NA,as.numeric(as.character(fluency)))) %>%
dplyr::group_by(group) %>%
dplyr::summarise(across(c("age","written","fluency"),
list(mean=mean,sd=sd))) %>%
dplyr::mutate(Age=paste0(format(round(age_mean,2),nsmall=2),
" (",
format(round(age_sd,2),nsmall=2),") "),
`Written Fluency`=paste0(
format(round(written_mean,2),nsmall=2),
" (",
format(round(written_sd,2),nsmall=2),
") "),
`Spoken Fluency`=paste0(
format(round(fluency_mean,2),nsmall=2),
" (",
format(round(fluency_sd,2),nsmall=2),
") "),
`Spoken Fluency`=ifelse(
group=="SG","",`Spoken Fluency`)) %>%
dplyr::select(!c(age_mean,age_sd,written_mean,written_sd,
fluency_mean,fluency_sd))Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `fluency = ifelse(fluency == "Yes", NA,
as.numeric(as.character(fluency)))`.
Caused by warning in `ifelse()`:
! NAs introduced by coercion
#summarise in a table
data.demo.merged.table<- as.data.frame(t(data.demo.merged.numeric)) %>%
row_to_names(row_number = 1) %>% #convert 1st row to colname
`rownames<-`(c("Age","Written Fluency","Summary")) %>%
#update `Spoken Fluency` for grouped rows
dplyr::mutate(SG=ifelse(SG=="","-",SG)) %>%
rbind(data.demo.merged.gender.fluency) %>%
kableExtra::kbl(booktabs = T, linesep = "",
#format = "latex",
format = "html",
label = "demo",
caption = "Summary of demographic information")%>%
pack_rows("Spoken Fluency", 3, 5) %>%
pack_rows("Gender", 6, 7) %>%
row_spec(3, hline_after = T) %>%
#kable_styling(latex_table_env = "tabularx") %>%
kable_styling(protect_latex = TRUE) %>%
kable_paper(full_width = T) #%>%
#save_kable(paste0(getwd(),"/Table tex files/demo.tex"))
data.demo.merged.table| MY:M | MY:O | SG | |
|---|---|---|---|
| Age | 24.00 (4.87) | 23.09 (2.64) | 25.91 (7.18) |
| Written Fluency | 8.74 (1.26) | 6.38 (1.56) | 8.06 (1.58) |
| Spoken Fluency | |||
| Summary | 9.48 (0.72) | 6.16 (1.53) | - |
| Yes | 31 | 17 | 32 |
| No | 0 | 15 | 0 |
| Gender | |||
| Female | 15 | 16 | 16 |
| Male | 16 | 16 | 16 |
Summary of demographic information
#summary of median values
data.merged.median<-data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::group_by(stimuliID,set) %>%
dplyr::summarise(across(pleasant:monotonous,
median,na.rm=TRUE)) %>%
pivot_longer(cols=-c(1:2),names_to = "PAQ",values_to = "median")Warning: There was 1 warning in `dplyr::summarise()`.
ℹ In argument: `across(pleasant:monotonous, median, na.rm = TRUE)`.
ℹ In group 1: `stimuliID = 1`, `set = "NTU"`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.
# Previously
across(a:b, mean, na.rm = TRUE)
# Now
across(a:b, \(x) mean(x, na.rm = TRUE))
#pivot to long table
data.merged.long<-data.satp.zsm2.l$data.subj.zsm2 %>%
pivot_longer(names_to = "PAQ",
values_to = "Score",
cols = c("pleasant":"monotonous"))
#ISOPL and ISOEV
data.ISOPLEV.median <- data.satp.zsm2.l$data.subj.zsm2 %>%
group_by(stimuliID,ETHNICITY) %>%
dplyr::summarise(across(c(ISOPL,ISOEV),
median,na.rm=TRUE))
#Median contour plot with median points of ISOPL and ISOEV
p.ISOPLEV.contour.facetedStimuli<-ggplot(data=data.satp.zsm2.l$data.subj.zsm2,
aes(x = ISOPL, y = ISOEV)) +
facet_wrap(~stimuliID, ncol = 9) +
# stat_density_2d(bins=3,contour_var = "ndensity",breaks=c(0.5),
# aes(color=ETHNICITY)) +
stat_density_2d(bins=3,contour_var = "ndensity",breaks=c(0.5),
geom = "density_2d",
aes(color=ETHNICITY)) +
geom_point(data = data.ISOPLEV.median,
aes(x = ISOPL, y = ISOEV, color=ETHNICITY)) +
ylim(c(-1,1)) + xlim(c(-1,1)) +
ggthemes::scale_colour_few() +
ylim(c(-1.1,1.1)) + xlim(c(-1.1,1.1))
p.ISOPLEV.contour.facetedStimuli
#KDE contour of all points
p.ISOPLEV.contour.all<-ggplot(data=data.satp.zsm2.l$data.subj.zsm2,
aes(x = ISOPL, y = ISOEV)) +
#facet_wrap(~stimuliID, ncol = 9) +
stat_density_2d(data=data.satp.zsm2.l$data.subj.zsm2,
geom = "density_2d",
alpha=0.7,
contour_var = "ndensity",
breaks=c(0.2),
aes(color=ETHNICITY,
fill=ETHNICITY,
alpha = stat(level))) +
stat_density_2d(data=data.araus,
geom = "density_2d",
alpha=0.5,
n=100,
contour_var = "ndensity",
breaks=c(0.2),
contour = TRUE,
color="#F17CB0",
linetype = "dashed") +
#geom_path(aes(x, y), data=contour_95) +
geom_point(data = data.ISOPLEV.median, alpha=0.3,
aes(x = ISOPL, y = ISOEV, color=ETHNICITY)) +
# geom_circle(aes(x0 = 0, y0 = 0, r = 1),
# fill = NA, color = "grey",
# linetype = "twodash") + # Add circles
#scale_colour_brewer(palette = "Set1") +
ggthemes::scale_colour_few() +
ylim(c(-1.2,1.2)) + xlim(c(-1.2,1.2)) +
theme(legend.position="bottom")Warning in stat_density_2d(data = data.satp.zsm2.l$data.subj.zsm2, geom =
"density_2d", : Ignoring unknown aesthetics: fill
#theme(legend.position="none")
p.ISOPLEV.contour.all.marg<-ggMarginal(p.ISOPLEV.contour.all,
groupColour = T,
groupFill = T,
alpha=0.15)Warning: `stat(level)` was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(level)` instead.
p.ISOPLEV.contour.all.marg
ggsave(paste0("./outputs/allcontour.pdf"),
plot = p.ISOPLEV.contour.all.marg,
width = 2300, height = 2350, units = "px",scale = 0.7)Due to a bug in the MATLAB GUI program, the same randomized participant order was presented to the participants whenever the MATLAB GUI program was restarted. Here, the NTU set is evaluated for order effects since some of the participants had the same randomized order but some were truly randomized.
#extract stimuli order from NTU group
NTUorder.df <- data.satp.zsm2.l$data.subj.zsm2 %>%
filter(set=="NTU") %>%
dplyr::select(participantID,stimuliID) %>%
pivot_wider(names_from = participantID, values_from = stimuliID)Warning: Values from `stimuliID` are not uniquely identified; output will contain
list-cols.
• Use `values_fn = list` to suppress this warning.
• Use `values_fn = {summary_fun}` to summarise duplicates.
• Use the following dplyr code to identify duplicates.
{data} %>%
dplyr::group_by(participantID) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::filter(n > 1L)
# Create a reference column (assuming it is the first column)
reference_column <- NTUorder.df$"8"[[1]]
# Initialize a list to store equivalent columns
equivalent_columns <- list()
# Loop through each column starting from the second column
for (i in 1:ncol(NTUorder.df)) {
# Compare each column to the reference column
if (all(reference_column == NTUorder.df[1, i][[1]][[1]])) {
equivalent_columns[[
length(equivalent_columns) + 1
]] <- as.numeric(colnames(NTUorder.df[1, i]))
}
}
# Extract the equivalent columns from the dataframe
same.order.pid <- unlist(equivalent_columns)
# Print the equivalent columns
cat("Participant IDs with the same order:\n")Participant IDs with the same order:
print(same.order.pid) [1] 8 21 23 24 25 26 28 31 32 34
#create new column to store
ks.order.df <- data.merged.long %>%
dplyr::filter(set=="NTU") %>%
dplyr::mutate(group=ifelse(
participantID %in% same.order.pid, "same", "random"),
across(c(stimuliID,PAQ),.fns = as.factor)) %>%
dplyr::group_by(PAQ,stimuliID) %>%
dplyr::summarize(
ks_test = list(ks.test(Score[group == "same"],
Score[group == "random"],
exact = NULL,
alternative = "two.sided")),
stat = ks_test[[1]]$statistic,
ks.pvalue = ks_test[[1]]$p.value,
ks.signif = ks.pvalue<0.05,
n.same = length(Score[group == "same"]),
n.rand = length(Score[group == "random"])) %>%
dplyr::ungroup() %>%
dplyr::mutate(ks.padj = p.adjust(ks.pvalue, method="BH"),
ks.adjsignif = ks.padj<0.05) %>%
dplyr::select(!ks_test)Warning: There were 216 warnings in `dplyr::summarize()`.
The first warning was:
ℹ In argument: `ks_test = list(...)`.
ℹ In group 1: `PAQ = annoying`, `stimuliID = 1`.
Caused by warning in `ks.test()`:
! cannot compute exact p-value with ties
ℹ Run `dplyr::last_dplyr_warnings()` to see the 215 remaining warnings.
cat("Number of KS comparisons p <0.05: ",
sum(ks.order.df$ks.signif),"/",
length(ks.order.df$ks.signif), "\n")Number of KS comparisons p <0.05: 10 / 216
cat("Number of KS comparisons with p.adj <0.05: ",
sum(ks.order.df$ks.adjsignif),"/",
length(ks.order.df$ks.adjsignif))Number of KS comparisons with p.adj <0.05: 0 / 216
Hence, there were no order effects present.
Kruskal-Wallis Test
#initialise data frame
data.kwt<-data.frame(stimuliID=numeric(),
PAQ=character(),
pvalue=numeric(),
effect=numeric())
list.PAQ<-c("eventful","vibrant","pleasant","calm",
"uneventful","monotonous","annoying","chaotic",
"ISOPL","ISOEV")
#for each stimuli
for(s.ID in 1:length(unique(data.satp.zsm2.l$data.subj.zsm2$stimuliID))){
#for each PAQ attribute
for (paq in list.PAQ){
df=data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(stimuliID==s.ID)
kwt<-kruskal.test(
as.formula(paste(paq,"~ETHNICITY")),
data=df)
kwteff<-kruskal_effsize(
formula = as.formula(paste(paq,"~ETHNICITY")),
data=df)
data.kwt<-rbind(
data.kwt,
c(stimuliID=s.ID,
PAQ=paq,
pvalue=kwt$p.value,
effect=kwteff$effsize))
}
}
colnames(data.kwt)<-c("stimuliID","PAQ","pvalue","effect")
#cases with significant differences
data.kwt.sig<-data.kwt %>%
dplyr::filter(as.numeric(pvalue)<0.05 & as.numeric(effect)>=0.01)
#export to csv
write.csv(x=data.kwt.sig,
file = "./outputs/SATP_Stage2_zsm_sigKWT.csv",
row.names = FALSE)Posthoc Conover-Iman Tests
#initialise data frame
data.cit<-data.frame(stimuliID=numeric(),
PAQ=character(),
stat=numeric(),
set=character(),
pvalue=numeric(),
adjpval=numeric())
#Perform CIT for significant cases in KWT
for(idx in 1:length(data.kwt.sig$stimuliID)){
paq<-data.kwt.sig$PAQ[idx]
x.ID<-data.kwt.sig$stimuliID[idx]
#select only kwt significant
df<-data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(stimuliID==x.ID)
cit<-conover.test(x=df[,paq],
g=df$ETHNICITY,
kw=FALSE,
method='bonferroni',
altp=TRUE)
data.cit<-rbind(
data.cit,
cbind(data.frame(stimuliID=x.ID,PAQ=paq),
as.data.frame(cit) %>%
dplyr::select(c("T",comparisons,
altP,altP.adjusted))))
} Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.096353
| 0.1164
|
SG | -0.579087 -2.696931
| 1.0000 0.0250*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.368721
| 0.0598
|
SG | 0.074384 -2.312765
| 1.0000 0.0689
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.551105
| 0.0372*
|
SG | -1.288147 -3.870090
| 0.6028 0.0006*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.374395
| 0.5180
|
SG | -2.042446 -3.444287
| 0.1319 0.0026*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.110177
| 0.8094
|
SG | -3.359556 -2.267446
| 0.0034* 0.0771
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -2.665952
| 0.0272*
|
SG | -1.518144 1.157028
| 0.3972 0.7508
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 3.487605
| 0.0022*
|
SG | 0.589962 -2.920917
| 1.0000 0.0132*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.364866
| 0.0604
|
SG | 2.650871 0.288301
| 0.0284* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.835132
| 0.0169*
|
SG | 2.168377 -0.672110
| 0.0981 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.815394
| 0.0179*
|
SG | 1.404042 -1.422688
| 0.4910 0.4746
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.575278
| 0.0348*
|
SG | 1.987977 -0.592018
| 0.1494 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.370473
| 0.0596
|
SG | 0.488495 -1.897094
| 1.0000 0.1829
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.767914
| 0.0205*
|
SG | 1.454815 -1.323646
| 0.4474 0.5667
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.577765
| 0.3542
|
SG | -2.685625 -1.116758
| 0.0258* 0.8010
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.941617
| 0.1657
|
SG | 4.846866 2.928584
| 0.0000* 0.0129*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.082022
| 0.1204
|
SG | 4.894541 2.835110
| 0.0000* 0.0169*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.821498
| 0.2153
|
SG | -3.319877 -1.510414
| 0.0039* 0.4031
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.382097
| 1.0000
|
SG | -4.526339 -4.177529
| 0.0001* 0.0002*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -2.412048
| 0.0535
|
SG | -4.283832 -1.886818
| 0.0001* 0.1870
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.556009
| 0.3694
|
SG | -2.952641 -1.407850
| 0.0120* 0.4876
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.505121
| 0.4072
|
SG | 5.127671 3.651647
| 0.0000* 0.0013*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -3.054735
| 0.0088*
|
SG | -3.135534 -0.081448
| 0.0069* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -3.240922
| 0.0050*
|
SG | -3.355987 -0.115989
| 0.0035* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.257033
| 1.0000
|
SG | 3.529800 3.817250
| 0.0020* 0.0007*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 0.075390
| 1.0000
|
SG | 3.621164 3.574254
| 0.0014* 0.0017*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.720999
| 0.0234*
|
SG | 0.680343 -2.057046
| 1.0000 0.1275
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 0.343194
| 1.0000
|
SG | -3.007546 -3.377654
| 0.0102* 0.0032*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.247375
| 1.0000
|
SG | -2.617850 -2.389515
| 0.0310* 0.0567
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.110394
| 0.8092
|
SG | -3.875555 -2.787371
| 0.0006* 0.0194*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.994770
| 0.9674
|
SG | 2.955386 3.981885
| 0.0119* 0.0004*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -3.757375
| 0.0009*
|
SG | -3.806249 -0.049266
| 0.0008* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.484189
| 0.4235
|
SG | -1.355782 -2.862782
| 0.5355 0.0156*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 3.087326
| 0.0080*
|
SG | 2.173749 -0.920914
| 0.0969 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.900857
| 0.0140*
|
SG | 1.053426 -1.862270
| 0.8847 0.1973
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.218424
| 0.6785
|
SG | -2.831497 -1.626029
| 0.0171* 0.3221
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 3.763649
| 0.0009*
|
SG | 3.977643 0.215712
| 0.0004* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.588748
| 1.0000
|
SG | -3.000999 -2.431627
| 0.0104* 0.0509
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.672797
| 1.0000
|
SG | -2.440599 -1.782001
| 0.0497* 0.2341
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 3.404160
| 0.0030*
|
SG | 2.144413 -1.269865
| 0.1039 0.6220
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.664143
| 0.0273*
|
SG | 2.877581 0.215151
| 0.0149* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.285967
| 0.6050
|
SG | -2.607179 -1.331823
| 0.0320* 0.5586
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.128625
| 0.7860
|
SG | -2.269567 -3.425487
| 0.0767 0.0028*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.945972
| 0.1641
|
SG | -3.230487 -1.294832
| 0.0051* 0.5959
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.237080
| 0.0831
|
SG | -0.307322 -2.564839
| 1.0000 0.0358*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.543273
| 0.0380*
|
SG | -0.367376 -2.934030
| 1.0000 0.0127*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.232436
| 1.0000
|
SG | -2.625746 -2.412534
| 0.0304* 0.0535
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -1.774008
| 0.2381
|
SG | -3.796499 -2.038736
| 0.0008* 0.1330
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.093543
| 0.8310
|
SG | 2.959157 1.880598
| 0.0118* 0.1896
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 3.342066
| 0.0036*
|
SG | 1.028624 -2.332024
| 0.9191 0.0656
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -2.548568
| 0.0374*
|
SG | -0.863477 1.698625
| 1.0000 0.2783
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.691147
| 0.0254*
|
SG | 1.518143 -1.182426
| 0.3972 0.7203
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.753876
| 0.0213*
|
SG | 2.224770 -0.533355
| 0.0856 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.402845
| 0.0548
|
SG | 0.434670 -1.983983
| 1.0000 0.1507
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.702042
| 0.0246*
|
SG | 1.096947 -1.617988
| 0.8266 0.3273
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.583714
| 0.0340*
|
SG | 2.116694 -0.470771
| 0.1110 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -0.191336
| 1.0000
|
SG | -2.678335 -2.506975
| 0.0263* 0.0418*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.814280
| 0.0179*
|
SG | 2.510167 -0.306555
| 0.0414* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 2.794392
| 0.0190*
|
SG | 3.103754 0.311847
| 0.0076* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 1.729741
| 0.2611
|
SG | -0.886247 -2.637001
| 1.0000 0.0295*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | 0.382518
| 1.0000
|
SG | -2.122087 -2.524722
| 0.1096 0.0399*
alpha = 0.05
Reject Ho if p <= alpha
Comparison of x by group
(Bonferroni)
Col Mean-|
Row Mean | MY:M MY:O
---------+----------------------
MY:O | -2.981636
| 0.0110*
|
SG | -2.928068 0.053997
| 0.0129* 1.0000
alpha = 0.05
Reject Ho if p <= alpha
#export significant cases
data.cit.sig<-data.cit %>%
filter(as.numeric(altP.adjusted)<0.05) %>%
dplyr::mutate(altP.adjusted=round(altP.adjusted,digits = 4))
write.csv(x=data.cit.sig,
file = "./outputs/SATP_Stage2_zsm_sigCIT.csv",
row.names = FALSE)#kruskal-wallis table of p-vales and effect sizes
kwtTable<-data.kwt %>%
pivot_longer(cols = c("pvalue","effect"),
names_to = "Stat",values_to = "Value") %>%
dplyr::mutate(Value=round(as.numeric(Value),4),
Value=case_when(Stat=="pvalue" & Value<0.0001~
paste0("****",formatC(Value,
format="f",
digits=4)),
Stat=="pvalue" & Value<0.001~
paste0("***",formatC(Value,
format="f",
digits=4)),
Stat=="pvalue" & Value<0.01~
paste0("**",formatC(Value,
format="f",
digits=4)),
Stat=="pvalue" & Value<0.05~
paste0("*",formatC(Value,
format="f",
digits=4)),
Stat=="effect" & abs(Value)>=0.01 & Value<0.06~
paste0("(S)",formatC(Value,
format="f",
digits=4)),
Stat=="effect" & abs(Value)>=0.06 & Value<0.14~
paste0("(M)",formatC(Value,
format="f",
digits=4)),
Stat=="effect" & abs(Value)>=0.14~
paste0("(L)",formatC(Value,
format="f",
digits=4)),
TRUE~formatC(Value,format="f",digits=4))) %>%
pivot_wider(names_from = "PAQ", values_from = "Value") %>%
kableExtra::kbl(booktabs = T, linesep = "",
#format = "latex",
format = "html",
label = "kwt",
caption = "Summary of Kruskal-Wallis Test")%>%
collapse_rows(columns = 1, valign = "top") %>%
#kable_styling(latex_table_env = "tabularx") %>%
kable_styling(protect_latex = TRUE) %>%
kable_paper(full_width = T) #%>%
#save_kable(paste0(getwd(),"/Table tex files/kwtTable.tex"))
kwtTable| stimuliID | Stat | eventful | vibrant | pleasant | calm | uneventful | monotonous | annoying | chaotic | ISOPL | ISOEV |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | pvalue | 0.8922 | 0.6866 | 0.0848 | *0.0229 | 0.8497 | 0.3356 | 0.8912 | 0.9657 | 0.5464 | 0.2345 |
| 1 | effect | (S)-0.0193 | (S)-0.0136 | (S)0.0319 | (M)0.0603 | (S)-0.0182 | 0.0020 | (S)-0.0192 | (S)-0.0210 | -0.0086 | 0.0098 |
| 2 | pvalue | 0.7010 | 0.5245 | 0.6466 | 0.9061 | *0.0312 | 0.4859 | 0.5070 | 0.1692 | 0.1697 | 0.2938 |
| 2 | effect | (S)-0.0140 | -0.0077 | (S)-0.0123 | (S)-0.0196 | (S)0.0536 | -0.0060 | -0.0070 | (S)0.0169 | (S)0.0168 | 0.0049 |
| 3 | pvalue | 0.7158 | 0.7277 | **0.0011 | **0.0044 | 0.6767 | **0.0048 | *0.0338 | 0.0966 | **0.0020 | 0.1467 |
| 3 | effect | (S)-0.0145 | (S)-0.0148 | (M)0.1256 | (M)0.0963 | (S)-0.0132 | (M)0.0941 | (S)0.0519 | (S)0.0291 | (M)0.1136 | (S)0.0200 |
| 4 | pvalue | *0.0194 | *0.0169 | 0.8589 | 0.9519 | 0.3967 | 0.3482 | 0.1311 | *0.0240 | 0.4322 | *0.0322 |
| 4 | effect | (M)0.0640 | (M)0.0670 | (S)-0.0184 | (S)-0.0207 | -0.0016 | 0.0012 | (S)0.0224 | (S)0.0593 | -0.0035 | (S)0.0529 |
| 5 | pvalue | 0.2984 | 0.5899 | 0.1018 | *0.0490 | 0.2225 | 0.5330 | 0.2140 | 0.0860 | *0.0269 | 0.3958 |
| 5 | effect | 0.0045 | (S)-0.0103 | (S)0.0279 | (S)0.0438 | (S)0.0109 | -0.0081 | (S)0.0118 | (S)0.0316 | (S)0.0568 | -0.0016 |
| 6 | pvalue | 0.7964 | *0.0319 | ***0.0001 | ***0.0001 | **0.0065 | ****0.0000 | ***0.0004 | *0.0171 | ****0.0000 | 0.7393 |
| 6 | effect | (S)-0.0168 | (S)0.0531 | (L)0.1887 | (L)0.1909 | (M)0.0878 | (L)0.1996 | (L)0.1488 | (M)0.0667 | (L)0.2162 | (S)-0.0152 |
| 7 | pvalue | **0.0033 | **0.0017 | ***0.0004 | ***0.0006 | *0.0226 | **0.0022 | *0.0193 | ***0.0009 | ***0.0006 | ***0.0003 |
| 7 | effect | (M)0.1023 | (M)0.1169 | (L)0.1464 | (L)0.1400 | (M)0.0606 | (M)0.1112 | (M)0.0640 | (M)0.1297 | (M)0.1386 | (L)0.1529 |
| 8 | pvalue | 0.0795 | 0.0670 | 0.9389 | 0.1881 | *0.0213 | 0.9626 | 0.6044 | 0.7504 | 0.8042 | 0.2756 |
| 8 | effect | (S)0.0333 | (S)0.0370 | (S)-0.0204 | (S)0.0146 | (M)0.0619 | (S)-0.0209 | (S)-0.0108 | (S)-0.0155 | (S)-0.0170 | 0.0063 |
| 9 | pvalue | 0.3322 | 0.8398 | 0.4943 | 0.4986 | 0.6499 | 0.5985 | 0.1561 | 0.3011 | 0.5723 | 0.7917 |
| 9 | effect | 0.0022 | (S)-0.0179 | -0.0064 | -0.0066 | (S)-0.0124 | (S)-0.0106 | (S)0.0186 | 0.0044 | -0.0096 | (S)-0.0167 |
| 10 | pvalue | 0.1339 | **0.0099 | 0.9309 | 0.2726 | 0.1489 | 0.5283 | 0.0799 | *0.0176 | 0.7436 | 0.1012 |
| 10 | effect | (S)0.0220 | (M)0.0787 | (S)-0.0202 | 0.0065 | (S)0.0197 | -0.0079 | (S)0.0332 | (M)0.0661 | (S)-0.0153 | (S)0.0281 |
| 11 | pvalue | 0.3501 | 0.0957 | 0.7691 | 0.5523 | 0.5499 | 0.8921 | *0.0225 | 0.6610 | 0.1256 | 0.1562 |
| 11 | effect | 0.0011 | (S)0.0293 | (S)-0.0160 | -0.0088 | -0.0087 | (S)-0.0193 | (M)0.0608 | (S)-0.0127 | (S)0.0234 | (S)0.0186 |
| 12 | pvalue | 0.1490 | ***0.0002 | **0.0092 | *0.0473 | 0.2027 | 0.4829 | 0.2888 | **0.0047 | 0.5712 | **0.0091 |
| 12 | effect | (S)0.0196 | (L)0.1601 | (M)0.0801 | (S)0.0446 | (S)0.0130 | -0.0059 | 0.0053 | (M)0.0946 | -0.0096 | (M)0.0805 |
| 13 | pvalue | 0.5336 | 0.4740 | 0.1234 | 0.1761 | 0.5949 | 0.9105 | 0.1603 | 0.3326 | 0.2151 | 0.3677 |
| 13 | effect | -0.0081 | -0.0055 | (S)0.0238 | (S)0.0160 | (S)-0.0105 | (S)-0.0197 | (S)0.0181 | 0.0022 | (S)0.0117 | 0.0000 |
| 14 | pvalue | 0.1339 | *0.0394 | 0.7714 | 0.4814 | 0.5111 | 0.9514 | 0.8511 | 0.8423 | 0.3714 | 0.6545 |
| 14 | effect | (S)0.0220 | (S)0.0486 | (S)-0.0161 | -0.0058 | -0.0071 | (S)-0.0207 | (S)-0.0182 | (S)-0.0180 | -0.0002 | (S)-0.0125 |
| 15 | pvalue | **0.0041 | **0.0079 | *0.0252 | **0.0093 | 0.1363 | 0.1101 | 0.3836 | 0.9885 | 0.0886 | *0.0185 |
| 15 | effect | (M)0.0976 | (M)0.0835 | (S)0.0582 | (M)0.0800 | (S)0.0216 | (S)0.0262 | -0.0009 | (S)-0.0215 | (S)0.0309 | (M)0.0649 |
| 16 | pvalue | 0.0611 | 0.1087 | 0.1161 | **0.0017 | 0.1949 | 0.5456 | 0.8784 | *0.0153 | 0.4645 | 0.1364 |
| 16 | effect | (S)0.0390 | (S)0.0265 | (S)0.0251 | (M)0.1169 | (S)0.0138 | -0.0086 | (S)-0.0189 | (M)0.0691 | -0.0051 | (S)0.0216 |
| 17 | pvalue | 0.1856 | **0.0048 | 0.9455 | 0.4601 | 0.2426 | 0.7042 | 0.1792 | 0.1366 | 0.7813 | 0.8305 |
| 17 | effect | (S)0.0149 | (M)0.0942 | (S)-0.0205 | -0.0049 | 0.0091 | (S)-0.0141 | (S)0.0156 | (S)0.0215 | (S)-0.0164 | (S)-0.0177 |
| 18 | pvalue | 0.7283 | 0.3410 | 0.6358 | 0.3277 | 0.2039 | 0.4012 | 0.0962 | 0.2346 | *0.0403 | 0.7897 |
| 18 | effect | (S)-0.0148 | 0.0016 | (S)-0.0119 | 0.0025 | (S)0.0128 | -0.0019 | (S)0.0292 | 0.0098 | (S)0.0481 | (S)-0.0166 |
| 19 | pvalue | 0.3957 | 0.1317 | 0.3188 | 0.1889 | 0.7906 | 0.0512 | 0.8993 | 0.4409 | 0.0797 | 0.7631 |
| 19 | effect | -0.0016 | (S)0.0223 | 0.0031 | (S)0.0145 | (S)-0.0166 | (S)0.0429 | (S)-0.0194 | -0.0039 | (S)0.0332 | (S)-0.0159 |
| 20 | pvalue | *0.0320 | *0.0188 | 0.5063 | 0.7328 | 0.1162 | 0.4963 | *0.0429 | 0.4304 | 0.7258 | *0.0302 |
| 20 | effect | (S)0.0531 | (M)0.0647 | -0.0069 | (S)-0.0150 | (S)0.0251 | -0.0065 | (S)0.0467 | -0.0034 | (S)-0.0148 | (S)0.0544 |
| 21 | pvalue | 0.4372 | 0.2387 | 0.1229 | 0.2525 | 0.3195 | 0.2694 | 0.6662 | 0.1848 | 0.5034 | 0.3455 |
| 21 | effect | -0.0038 | 0.0094 | (S)0.0238 | 0.0082 | 0.0031 | 0.0068 | (S)-0.0129 | (S)0.0150 | -0.0068 | 0.0014 |
| 22 | pvalue | *0.0283 | 0.3071 | 0.7082 | 0.4818 | 0.2880 | *0.0151 | 0.2179 | *0.0123 | 0.4669 | **0.0051 |
| 22 | effect | (S)0.0557 | 0.0039 | (S)-0.0142 | -0.0059 | 0.0053 | (M)0.0694 | (S)0.0114 | (M)0.0738 | -0.0052 | (M)0.0929 |
| 23 | pvalue | 0.9169 | 0.2876 | 0.6474 | 0.1744 | 0.3075 | 0.5249 | 0.1535 | 0.2067 | 0.3664 | 0.1745 |
| 23 | effect | (S)-0.0199 | 0.0054 | (S)-0.0123 | (S)0.0162 | 0.0039 | -0.0077 | (S)0.0190 | (S)0.0125 | 0.0001 | (S)0.0162 |
| 24 | pvalue | 0.3843 | 0.6265 | *0.0331 | *0.0305 | 0.2755 | 0.6393 | 0.4755 | 0.0738 | 0.1511 | 0.1465 |
| 24 | effect | -0.0009 | (S)-0.0116 | (S)0.0523 | (S)0.0541 | 0.0063 | (S)-0.0120 | -0.0056 | (S)0.0349 | (S)0.0193 | (S)0.0200 |
| 25 | pvalue | 0.0529 | **0.0052 | 0.6339 | 0.2347 | 0.9759 | 0.0857 | 0.1710 | 0.0545 | 0.1953 | 0.3919 |
| 25 | effect | (S)0.0421 | (M)0.0925 | (S)-0.0118 | 0.0098 | (S)-0.0212 | (S)0.0317 | (S)0.0167 | (S)0.0415 | (S)0.0138 | -0.0014 |
| 26 | pvalue | 0.0960 | 0.1459 | 0.9533 | 0.8900 | 0.1443 | 0.3838 | 0.9719 | 0.9136 | 0.7185 | 0.0536 |
| 26 | effect | (S)0.0292 | (S)0.0201 | (S)-0.0207 | (S)-0.0192 | (S)0.0203 | -0.0009 | (S)-0.0211 | (S)-0.0198 | (S)-0.0146 | (S)0.0419 |
| 27 | pvalue | 0.8821 | 0.8291 | 0.7761 | 0.3570 | 0.9775 | 0.4754 | 0.6613 | 0.2503 | 0.9078 | 0.7823 |
| 27 | effect | (S)-0.0190 | (S)-0.0177 | (S)-0.0162 | 0.0006 | (S)-0.0212 | -0.0056 | (S)-0.0127 | 0.0084 | (S)-0.0196 | (S)-0.0164 |
Summary of Kruskal-Wallis Test
#posthoc conover-iman table of p-vales
citTable <- data.cit %>%
dplyr::mutate(PAQ=factor(PAQ, level=list.PAQ),
comparisons=gsub(" - ","--",comparisons)) %>%
dplyr::select(!altP) %>%
dplyr::mutate(altP.adjusted=round(as.numeric(altP.adjusted),4),
altP.adjusted=case_when(altP.adjusted <0.0001~
paste0("****",formatC(altP.adjusted,
format="f",digits=4)),
altP.adjusted <0.001~
paste0("***",formatC(altP.adjusted,
format="f",digits=4)),
altP.adjusted <0.01~
paste0("**",formatC(altP.adjusted,
format="f",digits=4)),
altP.adjusted <0.05~
paste0("*",formatC(altP.adjusted,
format="f",digits=4)),
TRUE~formatC(altP.adjusted,format="f",digits=4))) %>%
pivot_wider(values_from = altP.adjusted, names_from = comparisons) %>%
kableExtra::kbl(booktabs = T, linesep = "",
#format = "latex",
format = "html",
label = "kwt",
caption = "Summary of Kruskal-Wallis Test")%>%
collapse_rows(columns = 1, valign = "top") %>%
#kable_styling(latex_table_env = "tabularx") %>%
kable_styling(protect_latex = TRUE) %>%
kable_paper(full_width = T) #%>%
#save_kable(paste0(getwd(),"/Table tex files/citTable.tex"))
citTable| stimuliID | PAQ | T | MY:M--MY:O | MY:M--SG | MY:O--SG |
|---|---|---|---|---|---|
| 1 | calm | 2.0963537 | 0.1164 | NA | NA |
| 1 | calm | -0.5790879 | NA | 1.0000 | NA |
| 1 | calm | -2.6969314 | NA | NA | *0.0250 |
| 2 | uneventful | 2.3687211 | 0.0598 | NA | NA |
| 2 | uneventful | 0.0743846 | NA | 1.0000 | NA |
| 2 | uneventful | -2.3127651 | NA | NA | 0.0689 |
| 3 | pleasant | 2.5511052 | *0.0372 | NA | NA |
| 3 | pleasant | -1.2881472 | NA | 0.6028 | NA |
| 3 | pleasant | -3.8700903 | NA | NA | ***0.0006 |
| 3 | calm | 1.3743958 | 0.5180 | NA | NA |
| 3 | calm | -2.0424465 | NA | 0.1319 | NA |
| 3 | calm | -3.4442873 | NA | NA | **0.0026 |
| 3 | monotonous | -1.1101775 | 0.8094 | NA | NA |
| 3 | monotonous | -3.3595569 | NA | **0.0034 | NA |
| 3 | monotonous | -2.2674469 | NA | NA | 0.0771 |
| 3 | annoying | -2.6659529 | *0.0272 | NA | NA |
| 3 | annoying | -1.5181442 | NA | 0.3972 | NA |
| 3 | annoying | 1.1570282 | NA | NA | 0.7508 |
| 3 | ISOPL | 3.4876052 | **0.0022 | NA | NA |
| 3 | ISOPL | 0.5899623 | NA | 1.0000 | NA |
| 3 | ISOPL | -2.9209175 | NA | NA | *0.0132 |
| 4 | eventful | 2.3648669 | 0.0604 | NA | NA |
| 4 | eventful | 2.6508714 | NA | *0.0284 | NA |
| 4 | eventful | 0.2883017 | NA | NA | 1.0000 |
| 4 | vibrant | 2.8351326 | *0.0169 | NA | NA |
| 4 | vibrant | 2.1683779 | NA | 0.0981 | NA |
| 4 | vibrant | -0.6721103 | NA | NA | 1.0000 |
| 4 | chaotic | 2.8153945 | *0.0179 | NA | NA |
| 4 | chaotic | 1.4040423 | NA | 0.4910 | NA |
| 4 | chaotic | -1.4226885 | NA | NA | 0.4746 |
| 4 | ISOEV | 2.5752788 | *0.0348 | NA | NA |
| 4 | ISOEV | 1.9879777 | NA | 0.1494 | NA |
| 4 | ISOEV | -0.5920185 | NA | NA | 1.0000 |
| 5 | calm | 2.3704737 | 0.0596 | NA | NA |
| 5 | calm | 0.4884955 | NA | 1.0000 | NA |
| 5 | calm | -1.8970947 | NA | NA | 0.1829 |
| 5 | ISOPL | 2.7679142 | *0.0205 | NA | NA |
| 5 | ISOPL | 1.4548153 | NA | 0.4474 | NA |
| 5 | ISOPL | -1.3236461 | NA | NA | 0.5667 |
| 6 | vibrant | -1.5777658 | 0.3542 | NA | NA |
| 6 | vibrant | -2.6856257 | NA | *0.0258 | NA |
| 6 | vibrant | -1.1167585 | NA | NA | 0.8010 |
| 6 | pleasant | 1.9416180 | 0.1657 | NA | NA |
| 6 | pleasant | 4.8468669 | NA | ****0.0000 | NA |
| 6 | pleasant | 2.9285846 | NA | NA | *0.0129 |
| 6 | calm | 2.0820223 | 0.1204 | NA | NA |
| 6 | calm | 4.8945418 | NA | ****0.0000 | NA |
| 6 | calm | 2.8351104 | NA | NA | *0.0169 |
| 6 | uneventful | -1.8214989 | 0.2153 | NA | NA |
| 6 | uneventful | -3.3198778 | NA | **0.0039 | NA |
| 6 | uneventful | -1.5104142 | NA | NA | 0.4031 |
| 6 | monotonous | -0.3820973 | 1.0000 | NA | NA |
| 6 | monotonous | -4.5263393 | NA | ***0.0001 | NA |
| 6 | monotonous | -4.1775297 | NA | NA | ***0.0002 |
| 6 | annoying | -2.4120484 | 0.0535 | NA | NA |
| 6 | annoying | -4.2838327 | NA | ***0.0001 | NA |
| 6 | annoying | -1.8868190 | NA | NA | 0.1870 |
| 6 | chaotic | -1.5560092 | 0.3694 | NA | NA |
| 6 | chaotic | -2.9526418 | NA | *0.0120 | NA |
| 6 | chaotic | -1.4078507 | NA | NA | 0.4876 |
| 6 | ISOPL | 1.5051212 | 0.4072 | NA | NA |
| 6 | ISOPL | 5.1276717 | NA | ****0.0000 | NA |
| 6 | ISOPL | 3.6516478 | NA | NA | **0.0013 |
| 7 | eventful | -3.0547352 | **0.0088 | NA | NA |
| 7 | eventful | -3.1355345 | NA | **0.0069 | NA |
| 7 | eventful | -0.0814483 | NA | NA | 1.0000 |
| 7 | vibrant | -3.2409225 | **0.0050 | NA | NA |
| 7 | vibrant | -3.3559876 | NA | **0.0035 | NA |
| 7 | vibrant | -0.1159893 | NA | NA | 1.0000 |
| 7 | pleasant | -0.2570331 | 1.0000 | NA | NA |
| 7 | pleasant | 3.5298006 | NA | **0.0020 | NA |
| 7 | pleasant | 3.8172506 | NA | NA | ***0.0007 |
| 7 | calm | 0.0753903 | 1.0000 | NA | NA |
| 7 | calm | 3.6211641 | NA | **0.0014 | NA |
| 7 | calm | 3.5742544 | NA | NA | **0.0017 |
| 7 | uneventful | 2.7209992 | *0.0234 | NA | NA |
| 7 | uneventful | 0.6803436 | NA | 1.0000 | NA |
| 7 | uneventful | -2.0570467 | NA | NA | 0.1275 |
| 7 | monotonous | 0.3431942 | 1.0000 | NA | NA |
| 7 | monotonous | -3.0075462 | NA | *0.0102 | NA |
| 7 | monotonous | -3.3776544 | NA | NA | **0.0032 |
| 7 | annoying | -0.2473751 | 1.0000 | NA | NA |
| 7 | annoying | -2.6178505 | NA | *0.0310 | NA |
| 7 | annoying | -2.3895156 | NA | NA | 0.0567 |
| 7 | chaotic | -1.1103947 | 0.8092 | NA | NA |
| 7 | chaotic | -3.8755554 | NA | ***0.0006 | NA |
| 7 | chaotic | -2.7873711 | NA | NA | *0.0194 |
| 7 | ISOPL | -0.9947703 | 0.9674 | NA | NA |
| 7 | ISOPL | 2.9553863 | NA | *0.0119 | NA |
| 7 | ISOPL | 3.9818853 | NA | NA | ***0.0004 |
| 7 | ISOEV | -3.7573760 | ***0.0009 | NA | NA |
| 7 | ISOEV | -3.8062498 | NA | ***0.0008 | NA |
| 7 | ISOEV | -0.0492664 | NA | NA | 1.0000 |
| 8 | uneventful | 1.4841892 | 0.4235 | NA | NA |
| 8 | uneventful | -1.3557822 | NA | 0.5355 | NA |
| 8 | uneventful | -2.8627828 | NA | NA | *0.0156 |
| 10 | vibrant | 3.0873265 | **0.0080 | NA | NA |
| 10 | vibrant | 2.1737500 | NA | 0.0969 | NA |
| 10 | vibrant | -0.9209146 | NA | NA | 1.0000 |
| 10 | chaotic | 2.9008575 | *0.0140 | NA | NA |
| 10 | chaotic | 1.0534261 | NA | 0.8847 | NA |
| 10 | chaotic | -1.8622705 | NA | NA | 0.1973 |
| 11 | annoying | -1.2184246 | 0.6785 | NA | NA |
| 11 | annoying | -2.8314971 | NA | *0.0171 | NA |
| 11 | annoying | -1.6260291 | NA | NA | 0.3221 |
| 12 | vibrant | 3.7636499 | ***0.0009 | NA | NA |
| 12 | vibrant | 3.9776434 | NA | ***0.0004 | NA |
| 12 | vibrant | 0.2157123 | NA | NA | 1.0000 |
| 12 | pleasant | -0.5887480 | 1.0000 | NA | NA |
| 12 | pleasant | -3.0009998 | NA | *0.0104 | NA |
| 12 | pleasant | -2.4316276 | NA | NA | 0.0509 |
| 12 | calm | -0.6727979 | 1.0000 | NA | NA |
| 12 | calm | -2.4406000 | NA | *0.0497 | NA |
| 12 | calm | -1.7820016 | NA | NA | 0.2341 |
| 12 | chaotic | 3.4041608 | **0.0030 | NA | NA |
| 12 | chaotic | 2.1444135 | NA | 0.1039 | NA |
| 12 | chaotic | -1.2698659 | NA | NA | 0.6220 |
| 12 | ISOEV | 2.6641439 | *0.0273 | NA | NA |
| 12 | ISOEV | 2.8775810 | NA | *0.0149 | NA |
| 12 | ISOEV | 0.2151516 | NA | NA | 1.0000 |
| 14 | vibrant | -1.2859679 | 0.6050 | NA | NA |
| 14 | vibrant | -2.6071791 | NA | *0.0320 | NA |
| 14 | vibrant | -1.3318235 | NA | NA | 0.5586 |
| 15 | eventful | 1.1286256 | 0.7860 | NA | NA |
| 15 | eventful | -2.2695672 | NA | 0.0767 | NA |
| 15 | eventful | -3.4254880 | NA | NA | **0.0028 |
| 15 | vibrant | -1.9459724 | 0.1641 | NA | NA |
| 15 | vibrant | -3.2304871 | NA | **0.0051 | NA |
| 15 | vibrant | -1.2948323 | NA | NA | 0.5959 |
| 15 | pleasant | 2.2370803 | 0.0831 | NA | NA |
| 15 | pleasant | -0.3073221 | NA | 1.0000 | NA |
| 15 | pleasant | -2.5648397 | NA | NA | *0.0358 |
| 15 | calm | 2.5432740 | *0.0380 | NA | NA |
| 15 | calm | -0.3673770 | NA | 1.0000 | NA |
| 15 | calm | -2.9340301 | NA | NA | *0.0127 |
| 15 | ISOEV | -0.2324360 | 1.0000 | NA | NA |
| 15 | ISOEV | -2.6257468 | NA | *0.0304 | NA |
| 15 | ISOEV | -2.4125344 | NA | NA | 0.0535 |
| 16 | calm | -1.7740088 | 0.2381 | NA | NA |
| 16 | calm | -3.7964997 | NA | ***0.0008 | NA |
| 16 | calm | -2.0387360 | NA | NA | 0.1330 |
| 16 | chaotic | 1.0935438 | 0.8310 | NA | NA |
| 16 | chaotic | 2.9591577 | NA | *0.0118 | NA |
| 16 | chaotic | 1.8805990 | NA | NA | 0.1896 |
| 17 | vibrant | 3.3420668 | **0.0036 | NA | NA |
| 17 | vibrant | 1.0286242 | NA | 0.9191 | NA |
| 17 | vibrant | -2.3320248 | NA | NA | 0.0656 |
| 18 | ISOPL | -2.5485685 | *0.0374 | NA | NA |
| 18 | ISOPL | -0.8634779 | NA | 1.0000 | NA |
| 18 | ISOPL | 1.6986257 | NA | NA | 0.2783 |
| 20 | eventful | 2.6911475 | *0.0254 | NA | NA |
| 20 | eventful | 1.5181430 | NA | 0.3972 | NA |
| 20 | eventful | -1.1824264 | NA | NA | 0.7203 |
| 20 | vibrant | 2.7538764 | *0.0213 | NA | NA |
| 20 | vibrant | 2.2247706 | NA | 0.0856 | NA |
| 20 | vibrant | -0.5333557 | NA | NA | 1.0000 |
| 20 | annoying | 2.4028451 | 0.0548 | NA | NA |
| 20 | annoying | 0.4346705 | NA | 1.0000 | NA |
| 20 | annoying | -1.9839835 | NA | NA | 0.1507 |
| 20 | ISOEV | 2.7020427 | *0.0246 | NA | NA |
| 20 | ISOEV | 1.0969471 | NA | 0.8266 | NA |
| 20 | ISOEV | -1.6179881 | NA | NA | 0.3273 |
| 22 | eventful | 2.5837148 | *0.0340 | NA | NA |
| 22 | eventful | 2.1166946 | NA | 0.1110 | NA |
| 22 | eventful | -0.4707715 | NA | NA | 1.0000 |
| 22 | monotonous | -0.1913366 | 1.0000 | NA | NA |
| 22 | monotonous | -2.6783357 | NA | *0.0263 | NA |
| 22 | monotonous | -2.5069753 | NA | NA | *0.0418 |
| 22 | chaotic | 2.8142804 | *0.0179 | NA | NA |
| 22 | chaotic | 2.5101674 | NA | *0.0414 | NA |
| 22 | chaotic | -0.3065556 | NA | NA | 1.0000 |
| 22 | ISOEV | 2.7943921 | *0.0190 | NA | NA |
| 22 | ISOEV | 3.1037543 | NA | **0.0076 | NA |
| 22 | ISOEV | 0.3118471 | NA | NA | 1.0000 |
| 24 | pleasant | 1.7297416 | 0.2611 | NA | NA |
| 24 | pleasant | -0.8862473 | NA | 1.0000 | NA |
| 24 | pleasant | -2.6370011 | NA | NA | *0.0295 |
| 24 | calm | 0.3825183 | 1.0000 | NA | NA |
| 24 | calm | -2.1220870 | NA | 0.1096 | NA |
| 24 | calm | -2.5247229 | NA | NA | *0.0399 |
| 25 | vibrant | -2.9816362 | *0.0110 | NA | NA |
| 25 | vibrant | -2.9280687 | NA | *0.0129 | NA |
| 25 | vibrant | 0.0539978 | NA | NA | 1.0000 |
Summary of Kruskal-Wallis Test
#box plots PAQ vs stimuli
#generate pairs and signif annotations for ggsnif plotting
boxplot.xtolerance<-0.25
signifbar.height.mym.myo<-110
signifbar.height.mym.sg<-135
signifbar.height.myo.sg<-110
#prepare dataframe for plotting significance brace
PAQ.combined.signif <- data.cit.sig %>%
dplyr::filter(!PAQ %in% c("ISOPL","ISOEV")) %>%
dplyr::mutate(stimuliID=as.numeric(stimuliID),
PAQ=case_when(PAQ == "eventful"~"e",
PAQ == "vibrant"~"v",
PAQ == "pleasant"~"p",
PAQ == "calm"~"ca",
PAQ == "uneventful"~"u",
PAQ == "monotonous"~"m",
PAQ == "annoying"~"a",
PAQ == "chaotic"~"ch"),
PAQ=factor(PAQ, level=c("e","v",
"p","ca","u",
"m","a",
"ch")),
#factor order for x-axis location
#PAQfctorder=as.numeric(PAQ),
PAQfctorder=as.numeric(stimuliID),
#MY:M is left most boxplot in group; MY:O is the middle
#x is the left edge of the signif bar
x=ifelse(grepl("MY:M \\-",comparisons),
PAQfctorder-boxplot.xtolerance,
PAQfctorder),
#xend is the right edge of the signif bar
xend=ifelse(grepl("- MY:O",comparisons),
PAQfctorder,PAQfctorder+boxplot.xtolerance),
y=ifelse(grepl("MY:M \\-",comparisons),
ifelse(grepl("- MY:O",comparisons),
signifbar.height.mym.myo,
signifbar.height.mym.sg),
signifbar.height.myo.sg),
#yend=y-5,
ann.labels=ifelse(
altP.adjusted<0.0001,
"****",
ifelse(altP.adjusted<0.001,
"***",
ifelse(altP.adjusted<0.01,
"**",
ifelse(altP.adjusted<0.05,
"*","ns")))),
stimuliID=factor(stimuliID,levels=c(1:27)))
#str(PAQ.combined.signif)
groupingFactor<-1
plotGroup<-1
totalStimuli<-length(unique(data.merged.long$stimuliID))
uniqueStimuli<-unique(data.merged.long$stimuliID)
stimuliGrps<-split(sort(uniqueStimuli),
ceiling(seq_along(uniqueStimuli)/
(totalStimuli/groupingFactor)))
p.8PAQ.box<-ggplot(data = data.merged.long %>%
mutate(stimuliID=as.factor(stimuliID),
PAQ=case_when(PAQ == "eventful"~"e",
PAQ == "vibrant"~"v",
PAQ == "pleasant"~"p",
PAQ == "calm"~"ca",
PAQ == "uneventful"~"u",
PAQ == "monotonous"~"m",
PAQ == "annoying"~"a",
PAQ == "chaotic"~"ch"),
PAQ=factor(PAQ, level=c("e","v",
"p","ca","u",
"m","a",
"ch"))) %>%
dplyr::filter(
stimuliID %in% stimuliGrps[[plotGroup]]),
aes(x = stimuliID, y = Score)) +
geom_boxplot(aes(fill=ETHNICITY)) +
geom_signif(data=PAQ.combined.signif %>%
dplyr::filter(grepl("MY:M -",comparisons)) %>%
dplyr::filter(
stimuliID %in% stimuliGrps[[plotGroup]] &
grepl("MY:M -",comparisons)),
inherit.aes = F,
mapping=aes(xmin=x,xmax=xend,y_position=y,
annotations=ann.labels,group=stimuliID),
textsize = 4 ,color="black",vjust = 0.4,
tip_length = 0.05, manual=T) +
geom_signif(data=PAQ.combined.signif %>%
dplyr::filter(grepl("MY:O -",comparisons)) %>%
dplyr::filter(
stimuliID %in% stimuliGrps[[plotGroup]] &
grepl("MY:O -",comparisons)),
inherit.aes = F,
mapping=aes(xmin=x, xmax=xend, y_position=y,
annotations=ann.labels,group=stimuliID),
textsize = 4 ,color="black",vjust = 0.4,
tip_length = 0.05, manual=T) +
ylim(c(0,140)) + xlab("Stimuli") +
facet_wrap(facets = ~PAQ, ncol = 1, strip.position="right") +
theme(legend.position="bottom")Warning in geom_signif(data = PAQ.combined.signif %>% dplyr::filter(grepl("MY:M
-", : Ignoring unknown aesthetics: xmin, xmax, y_position, and annotations
Warning in geom_signif(data = PAQ.combined.signif %>% dplyr::filter(grepl("MY:O
-", : Ignoring unknown aesthetics: xmin, xmax, y_position, and annotations
p.8PAQ.boxWarning: The following aesthetics were dropped during statistical transformation: xmax,
y_position
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
Warning: The following aesthetics were dropped during statistical transformation: xmax,
y_position
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation: xmax,
y_position
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
#ggsave(paste0("boxplots.pdf"),plot = p.8PAQ.box, width = 1700,
# height = 2300, units = "px",scale = 1.4)
ggsave(paste0("./outputs/boxplots.pdf"),
plot = p.8PAQ.box, width = 2300,
height = 2300, units = "px",scale = 1.4)Warning: The following aesthetics were dropped during statistical transformation: xmax,
y_position
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation: xmax,
y_position
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation: xmax,
y_position
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
#Plot ISOPL and ISOEV contour plot
ISOPL.signif <- data.cit.sig %>%
dplyr::filter(PAQ %in% c("ISOPL","ISOEV")) %>%
dplyr::mutate(stimuliID=as.numeric(stimuliID))
ISOPLEV.combined.signif <- data.cit.sig %>%
#only ISOPL and ISOEV for plotting signif in contours
dplyr::filter(PAQ %in% c("ISOPL","ISOEV")) %>%
#extract comparison pair
dplyr::mutate(stimuliID=as.numeric(stimuliID),
#left comparison pair
ETHNICITY=ifelse(grepl("SG \\-",comparisons),"SG",
ifelse(grepl("MY:O -",comparisons),
"MY:O","MY:M"))) %>%
#retrieve ISOPL and ISOEV median values of 1st comparison pair
left_join(data.ISOPLEV.median,by=c("stimuliID","ETHNICITY")) %>%
#update colname to reflect PAIR1
dplyr::mutate(PAIR1.ETHNICITY=ETHNICITY,
PAIR1.ISOPL=ISOPL,
PAIR1.ISOEV=ISOEV,
#right comparison pair
ETHNICITY=ifelse(grepl("- SG",comparisons),"SG",
ifelse(grepl("- MY:O",comparisons),
"MY:O","MY:M")), .keep="unused") %>%
#retrieve ISOPL and ISOEV median values of 2nd comparison pair
left_join(data.ISOPLEV.median,by=c("stimuliID","ETHNICITY")) %>%
#update colname to reflect PAIR2
dplyr::mutate(PAIR2.ETHNICITY=ETHNICITY,
PAIR2.ISOPL=ISOPL,
PAIR2.ISOEV=ISOEV,
.keep="unused") %>%
#generate significance labels
dplyr::mutate(ann.labels=ifelse(
altP.adjusted<0.0001,
"****",
ifelse(altP.adjusted<0.001,
"***",
ifelse(altP.adjusted<0.01,
"**",
ifelse(altP.adjusted<0.05,"*","ns")))))
#create dataframe for ISOEV signif brace plotting
temp.df<-ISOPLEV.combined.signif %>% filter(PAQ=="ISOEV") %>%
pivot_longer(cols = c("PAIR1.ISOEV","PAIR2.ISOEV"),
values_to = c("ISOEV")) %>%
dplyr::select(c(ISOEV))
brace.df<-ISOPLEV.combined.signif %>% filter(PAQ=="ISOEV") %>%
pivot_longer(cols = c("PAIR1.ISOPL","PAIR2.ISOPL"),
values_to = c("ISOPL")) %>%
cbind(.,temp.df)
p.ISOPLEV.contour.signif<-p.ISOPLEV.contour.facetedStimuli +
#draw signif braces for ISOPL
geom_signif(data = ISOPLEV.combined.signif %>% filter(PAQ=="ISOPL"),
inherit.aes = F,
aes(y_position=c(0.5,0.9,0.2,0.4,0.8,0.6,0.2,0.3),
xmin=PAIR1.ISOPL,
xmax=PAIR2.ISOPL,
annotations = ann.labels),
manual = T) +
#draw signif braces for ISOEV
#right side brace: stimuli 4
stat_brace(data = brace.df %>% filter(stimuliID %in% c(4,20)),
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 90, labelrotate = 90, labelsize = 5,
distance = 0.2,width = 0.2,bending=0.01) +
#left side brace: stimuli 15
stat_brace(data = brace.df %>% filter(stimuliID == 15),
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 270, labelrotate = 270, labelsize = 5,
labeldistance = 0.3,
distance = 0.5,width = 0.2,bending=0.01) +
#right side brace: stimuli 7
stat_brace(data = brace.df %>% filter(stimuliID == 7) %>% .[1:2,],
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 90, labelrotate = 90, labelsize = 5,
labeldistance = 0.1,
distance = 0.05,width = 0.2,bending=0.01)+
#left side brace: stimuli 7
stat_brace(data = brace.df %>% filter(stimuliID == 7) %>% .[3:4,],
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 270, labelrotate = 270, labelsize = 5,
labeldistance = 0.3,
distance = 0.4,width = 0.2,bending=0.01) +
#right side brace: stimuli 12
stat_brace(data = brace.df %>% filter(stimuliID == 12) %>% .[1:2,],
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 90, labelrotate = 90, labelsize = 5,
labeldistance = 0.1,
distance = 0.2,width = 0.2,bending=0.01) +
#left side brace: stimuli 12
stat_brace(data = brace.df %>% filter(stimuliID == 12) %>% .[3:4,],
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 270, labelrotate = 270, labelsize = 5,
labeldistance = 0.2,
distance = 0.4,width = 0.2,bending=0.01) +
#right side brace: stimuli 22
stat_brace(data = brace.df %>% filter(stimuliID == 22) %>% .[1:2,],
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 90, labelrotate = 90, labelsize = 5,
distance = 0.1,width = 0.2,bending=0.01) +
#left side brace: stimuli 22
stat_brace(data = brace.df %>% filter(stimuliID == 22) %>% .[3:4,],
mapping=aes(x=ISOPL,y=ISOEV,
label=ann.labels),
inherit.aes = F,
rotate = 270, labelrotate = 270, labelsize = 5,
labeldistance = 0.2,
distance = 0.25,width = 0.2,bending=0.01) +
theme(legend.position = "bottom",
axis.text.x=element_text(angle = 90, vjust = 0.5, hjust=1))Warning in geom_signif(data = ISOPLEV.combined.signif %>% filter(PAQ == :
Ignoring unknown aesthetics: y_position, xmin, xmax, and annotations
p.ISOPLEV.contour.signif
ggsave("./outputs/ISOPLEVMedianContourNew.pdf",
plot = p.ISOPLEV.contour.signif,
width = 2500, height = 1150, units = "px",scale = 1.4)#perform PCA on 8 attributes
#my:m group
data.merged.mym<-data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="MY:M") %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic))
data.merged.mym.cor<-cor(data.merged.mym)
#my:o group
data.merged.myo<-data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="MY:O") %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic))
data.merged.myo.cor<-cor(data.merged.myo)
#sg group
data.merged.sg<-data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="SG") %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic))
data.merged.sg.cor<-cor(data.merged.sg)
#araus dataset
data.araus.cor<-cor(
data.araus %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic)))#KMO test
kmo<-rbind(KMO(data.merged.mym.cor)$MSA %>% as.data.frame() %>%
mutate(ETHNICITY="MY:M"),
KMO(data.merged.myo.cor)$MSA %>% as.data.frame() %>%
mutate(ETHNICITY="MY:O"),
KMO(data.merged.sg.cor)$MSA %>% as.data.frame() %>%
mutate(ETHNICITY="SG"),
KMO(data.araus.cor)$MSA %>% as.data.frame() %>%
mutate(ETHNICITY="ARAUS")) %>%
`colnames<-`(c("MSA","ETHNICITY"))
kmo MSA ETHNICITY
1 0.8148017 MY:M
2 0.7543526 MY:O
3 0.7701534 SG
4 0.8046084 ARAUS
#Bartlett's Test of Sphericity
spher<-rbind(cortest.bartlett(data.merged.mym.cor,
n = nrow(data.merged.mym))$p.value %>%
as.data.frame() %>%
dplyr::mutate(ETHNICITY="MY:M"),
cortest.bartlett(data.merged.myo.cor,
n = nrow(data.merged.myo))$p.value %>%
as.data.frame() %>%
dplyr::mutate(ETHNICITY="MY:O"),
cortest.bartlett(data.merged.sg.cor,
n = nrow(data.merged.sg))$p.value %>%
as.data.frame() %>%
dplyr::mutate(ETHNICITY="SG"),
cortest.bartlett(data.araus.cor,
n = nrow(data.araus))$p.value %>%
as.data.frame() %>%
dplyr::mutate(ETHNICITY="ARAUS")) %>%
`colnames<-`(c("p-value","ETHNICITY"))
spher p-value ETHNICITY
1 0.000000e+00 MY:M
2 0.000000e+00 MY:O
3 0.000000e+00 SG
4 1.192315e-161 ARAUS
#PCA of 8 paq for MY:M
paq.pca.MYM <- data.merged.mym %>%
prcomp(center = TRUE,scale. = TRUE,retx = TRUE)
#reflect y-axis
paq.pca.MYM$rotation[,2]<-paq.pca.MYM$rotation[,2]*-1
#plot PCA variables
paq.pca.MYM.p<-fviz_pca_var(paq.pca.MYM,
col.var = "darkred",
repel = TRUE # Avoid text overlapping
)
paq.pca.MYM.p
#PCA of 8 paq for MY:O
paq.pca.MYO <- data.merged.myo %>%
prcomp(center = TRUE,scale. = TRUE,retx = TRUE)
#plot PCA variables
paq.pca.MYO.p<-fviz_pca_var(paq.pca.MYO, col.var = "steelblue",
repel = TRUE # Avoid text overlapping
)
paq.pca.MYO.p
#PCA of 8 paq for SG
paq.pca.SG <- data.merged.sg %>%
prcomp(center = TRUE,scale. = TRUE,retx = TRUE)
# #reflect x-axis and y-axis i.e. rotate 180deg
# paq.pca.SG$rotation[,1]<-paq.pca.SG$rotation[,1]*-1
# paq.pca.SG$rotation[,2]<-paq.pca.SG$rotation[,2]*-1
#plot PCA variables
paq.pca.SG.p<-fviz_pca_var(paq.pca.SG, col.var = "forestgreen",
repel = TRUE # Avoid text overlapping
)
paq.pca.SG.p
#PCA of 8 paq for SG
paq.pca.ARAUS <- data.araus %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic)) %>%
prcomp(center = TRUE,scale. = TRUE,retx = TRUE)
#plot PCA variables
paq.pca.ARAUS.p<-fviz_pca_var(paq.pca.ARAUS, col.var = "maroon",
repel = TRUE # Avoid text overlapping
)
paq.pca.ARAUS.p
#summarise PCA data for plotting
pca.paq<-rbind(facto_summarize(paq.pca.SG, "var", axes = 1:2)[,-1] %>%
rownames_to_column(var = "PAQ") %>%
dplyr::mutate(ETHNICITY="SG",
#reflect x-axis and y-axis i.e. rotate 180deg
Dim.1=Dim.1*-1),
#Dim.2=Dim.2*-1),
facto_summarize(paq.pca.MYO, "var", axes = 1:2)[,-1] %>%
rownames_to_column(var = "PAQ") %>%
dplyr::mutate(ETHNICITY="MY:O"),
facto_summarize(paq.pca.MYM, "var", axes = 1:2)[,-1] %>%
rownames_to_column(var = "PAQ") %>%
dplyr::mutate(ETHNICITY="MY:M",
Dim.1=Dim.1*-1),
facto_summarize(paq.pca.ARAUS, "var", axes = 1:2)[,-1] %>%
rownames_to_column(var = "PAQ") %>%
dplyr::mutate(ETHNICITY="ARAUS",
Dim.1=Dim.1*-1))
#create rotation matrix for MY:M based on pleasantness at 90 deg
# Rotation angle in radians
rotation.angle.mym <- -atan(
pca.paq[pca.paq$ETHNICITY=="MY:M" &
pca.paq$PAQ=="pleasant","Dim.2"]/
pca.paq[pca.paq$ETHNICITY=="MY:M" &
pca.paq$PAQ=="pleasant","Dim.1"])
# Create rotation matrix
rotation.matrix.mym <- matrix(c(cos(rotation.angle.mym),
-sin(rotation.angle.mym),
sin(rotation.angle.mym),
cos(rotation.angle.mym)),
nrow = 2, ncol = 2, byrow = TRUE)
# Transpose the endpoint coordinates matrix
endpoint.coordinates.mym <- t(as.matrix(
pca.paq[pca.paq$ETHNICITY=="MY:M",c("Dim.1","Dim.2")]))
# Apply rotation to the endpoint coordinates
rotated.coordinates.mym <- rotation.matrix.mym %*% endpoint.coordinates.mym
#create rotation matrix for ARUAS based on pleasantness at 90 deg
# Rotation angle in radians
rotation.angle.araus <- -atan(pca.paq[pca.paq$ETHNICITY=="ARAUS" &
pca.paq$PAQ=="pleasant",
"Dim.2"]/
pca.paq[pca.paq$ETHNICITY=="ARAUS" &
pca.paq$PAQ=="pleasant","Dim.1"])
# Create rotation matrix
rotation.matrix.araus <- matrix(c(cos(rotation.angle.araus),
-sin(rotation.angle.araus),
sin(rotation.angle.araus),
cos(rotation.angle.araus)),
nrow = 2, ncol = 2, byrow = TRUE)
# Transpose the endpoint coordinates matrix
endpoint.coordinates.araus <- t(as.matrix(pca.paq[pca.paq$ETHNICITY=="ARAUS",c("Dim.1","Dim.2")]))
# Apply rotation to the endpoint coordinates
rotated.coordinates.araus <- rotation.matrix.araus %*% endpoint.coordinates.araus
#flip along x-axis
rotated.coordinates.araus[2,] <- rotated.coordinates.araus[2,]*-1
pca.paq.rotated <- pca.paq
pca.paq.rotated[pca.paq.rotated$ETHNICITY=="MY:M",c("Dim.1","Dim.2")] <-
t(rotated.coordinates.mym)
pca.paq.rotated[pca.paq.rotated$ETHNICITY=="ARAUS",c("Dim.1","Dim.2")] <-
t(rotated.coordinates.araus)
#plot PCA of three groups separately in subplots
p.pca<-ggplot(
pca.paq.rotated %>%
mutate(ETHNICITY=factor(
ETHNICITY,
levels = c("MY:M","MY:O","SG","ARAUS"))),
aes(x = Dim.1, y = Dim.2)) +
geom_segment(aes(x=0, y=0,
xend=Dim.1, yend=Dim.2,
color = PAQ,
linetype = PAQ),
# Add arrows with conditional formatting
arrow = arrow(length = unit(0.25, "cm"),
type = "closed",
),
size = 1,linejoin='mitre') +
geom_text(aes(label = PAQ),
nudge_x = 0.1,
nudge_y = 0.1,
size = 4) + # Add labels to arrows
geom_circle(aes(x0 = 0, y0 = 0, r = 1),
fill = NA, color = "grey",
linetype = "dashed") + # Add circles
facet_wrap(~ ETHNICITY, ncol = 2) +
scale_color_manual(values = c("black", "darkgrey",
"darkgrey", "black", "darkgrey",
"black", "black",
"darkgrey")) + # Specify color scale
scale_linetype_manual(values = c("solid", "dashed",
"dashed", "solid",
"dashed", "solid",
"solid",
"dashed")) +
# Specify linetype scale
labs(x = "PC1", y = "PC2", color = "PAQ", linetype = "PAQ") +
xlim(c(-1.2,1.2)) + ylim(c(-1.2,1.2))+
theme_minimal() + theme(legend.position = "none",
text = element_text(size = 16))Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
p.pcaWarning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
ggsave(paste0("./outputs/PCAprojections_araus.pdf"),
plot = p.pca, width = 4200,
height = 4300, units = "px",scale = 1)# #RTHORR test
res.rthorr<-RTHORR::randmf_from_df(
df_list = list(cor(data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="MY:M") %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic))),
cor(data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="MY:O") %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic))),
cor(data.satp.zsm2.l$data.subj.zsm2 %>%
dplyr::filter(ETHNICITY=="SG") %>%
dplyr::select(c(eventful,vibrant,
pleasant,calm,
uneventful,monotonous,
annoying,chaotic))),
data.araus.cor),
ord = "circular8")
ci.rthorr <- cbind(res.rthorr$RTHOR %>% as.data.frame(),
data.frame(ETHNICITY=c("MY:M","MY:O","SG","ARAUS")))
#ci.rthorr#SSM circumplex tests
# Multiple-group mean-based SSM
res.ssm.mean <- ssm_analyze(
.data = rbind(data.satp.zsm2.l$data.subj.zsm2 %>%
#dplyr::filter(ETHNICITY=="MY:M") %>%
dplyr::select(c(pleasant:monotonous),
ISOPL,ISOEV,ETHNICITY) %>%
dplyr::mutate(across(c(pleasant:monotonous),
function(x) x/100)),
data.araus %>%
dplyr::select(c(eventful:chaotic,
ISOPL,ISOEV)) %>%
dplyr::mutate(across(c(eventful:chaotic),
function(x) x/5)) %>%
dplyr::mutate(ETHNICITY="ARAUS")),
scales = pleasant:monotonous,
angles = c(0,135,45,270,315,180,90,225),
#angles = c(90,315,45,180,135,270,0,225),
grouping = ETHNICITY)
#ssm_table(res.ssm.mean)
#summary(res.ssm.mean)#CircE: CFI, RMSEA, SRMR
#equal angle only
circE.MYM.ea=CircE.BFGS(data.merged.mym.cor,
v.names = rownames(data.merged.mym.cor),
m=2,N=n.participsnts.MY.M,r=1, equal.ang = TRUE)Date: Wed Jul 5 14:09:39 2023
Data: Circumplex Estimation
Model:Equal spacing
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.0380179 3.7313385 Inf 0
a 2 0.0000000 22.9587076 Inf 0
v eventful 0.4388344 1.2252922 Inf 0
v vibrant 0.4114001 5.4930327 Inf 0
v pleasant 0.1041365 4.5546684 Inf 0
v calm 0.1158413 4.0707495 Inf 0
v uneventful 0.6357560 1.1970479 Inf 0
v monotonous 0.8890249 0.7361073 Inf 0
v annoying 0.3290793 0.4031439 Inf 0
v chaotic 0.2566440 0.6761643 Inf 0
z eventful 0.8043718 1.4974969 Inf 0
z vibrant 0.8152370 4.3899564 Inf 0
z pleasant 0.9492864 2.1522028 Inf 0
z calm 0.9437553 0.2892228 Inf 0
z uneventful 0.7314296 2.1736711 Inf 0
z monotonous 0.6498327 2.6159450 Inf 0
z annoying 0.8489066 0.5507897 Inf 0
z chaotic 0.8798776 -0.4494478 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 4.253912
iter 2 value 4.135488
iter 3 value 3.851312
iter 4 value 3.739621
iter 5 value 3.659641
iter 6 value 3.558081
iter 7 value 3.034727
iter 8 value 2.787209
iter 9 value 2.717687
iter 10 value 2.639580
iter 11 value 2.536190
iter 12 value 2.382379
iter 13 value 2.359213
iter 14 value 2.283361
iter 15 value 2.254763
iter 16 value 2.232083
iter 17 value 2.212695
iter 18 value 2.184768
iter 19 value 2.181685
iter 20 value 2.174682
iter 21 value 2.172984
iter 22 value 2.171844
iter 23 value 2.168581
iter 24 value 2.167648
iter 25 value 2.157038
iter 26 value 2.135608
iter 27 value 2.080136
iter 28 value 2.045851
iter 29 value 2.020840
iter 30 value 2.002402
iter 31 value 1.987183
iter 32 value 1.979642
iter 33 value 1.973735
iter 34 value 1.966721
iter 35 value 1.963065
iter 36 value 1.958917
iter 37 value 1.953838
iter 38 value 1.948710
iter 39 value 1.945219
iter 40 value 1.942792
iter 41 value 1.940956
iter 42 value 1.937420
iter 43 value 1.936437
iter 44 value 1.934854
iter 45 value 1.933895
iter 46 value 1.932334
iter 47 value 1.930212
iter 48 value 1.929332
iter 49 value 1.928360
iter 50 value 1.927054
iter 51 value 1.924926
iter 52 value 1.922805
iter 53 value 1.922379
iter 54 value 1.922156
iter 55 value 1.921635
iter 56 value 1.919601
iter 57 value 1.917565
iter 58 value 1.912968
iter 59 value 1.910746
iter 60 value 1.909039
iter 61 value 1.907967
iter 62 value 1.906023
iter 63 value 1.905797
iter 64 value 1.905593
iter 65 value 1.903913
iter 66 value 1.902702
iter 67 value 1.902365
iter 68 value 1.902117
iter 69 value 1.901988
iter 70 value 1.901775
iter 71 value 1.901344
iter 72 value 1.900864
iter 73 value 1.900276
iter 74 value 1.899600
iter 75 value 1.899169
iter 76 value 1.898859
iter 77 value 1.898423
iter 78 value 1.898005
iter 79 value 1.897675
iter 80 value 1.897398
iter 81 value 1.896652
iter 82 value 1.895948
iter 83 value 1.895128
iter 84 value 1.894075
iter 85 value 1.893593
iter 86 value 1.892664
iter 87 value 1.892010
iter 88 value 1.891468
iter 89 value 1.891299
iter 90 value 1.891033
iter 91 value 1.890873
iter 92 value 1.890745
iter 93 value 1.890290
iter 94 value 1.889885
iter 95 value 1.889402
iter 96 value 1.888255
iter 97 value 1.886175
iter 98 value 1.881710
iter 99 value 1.878946
iter 100 value 1.875622
iter 101 value 1.871555
iter 102 value 1.858484
iter 103 value 1.855635
iter 104 value 1.849696
iter 105 value 1.846415
iter 106 value 1.841304
iter 107 value 1.839146
iter 108 value 1.836756
iter 109 value 1.834186
iter 110 value 1.832854
iter 111 value 1.830828
iter 112 value 1.828329
iter 113 value 1.824580
iter 114 value 1.820136
iter 115 value 1.816639
iter 116 value 1.814807
iter 117 value 1.813379
iter 118 value 1.809053
iter 119 value 1.805838
iter 120 value 1.802833
iter 121 value 1.801496
iter 122 value 1.799614
iter 123 value 1.796261
iter 124 value 1.792642
iter 125 value 1.791060
iter 126 value 1.789091
iter 127 value 1.788005
iter 128 value 1.786859
iter 129 value 1.785462
iter 130 value 1.784824
iter 131 value 1.784275
iter 132 value 1.783700
iter 133 value 1.782893
iter 134 value 1.781936
iter 135 value 1.781108
iter 136 value 1.780647
iter 137 value 1.779776
iter 138 value 1.778819
iter 139 value 1.778510
iter 140 value 1.778263
iter 141 value 1.778089
iter 142 value 1.777778
iter 143 value 1.776958
iter 144 value 1.775912
iter 145 value 1.775277
iter 146 value 1.774511
iter 147 value 1.774148
iter 148 value 1.773871
iter 149 value 1.773281
iter 150 value 1.772374
iter 151 value 1.771353
iter 152 value 1.770833
iter 153 value 1.770040
iter 154 value 1.769872
iter 155 value 1.769645
iter 156 value 1.769535
iter 157 value 1.769399
iter 158 value 1.769274
iter 159 value 1.769025
iter 160 value 1.768660
iter 161 value 1.768524
iter 162 value 1.768285
iter 163 value 1.767979
iter 164 value 1.767678
iter 165 value 1.767490
iter 166 value 1.767351
iter 167 value 1.767302
iter 168 value 1.767273
iter 169 value 1.767213
iter 170 value 1.767105
iter 171 value 1.766889
iter 172 value 1.766624
iter 173 value 1.766442
iter 174 value 1.766270
iter 175 value 1.766083
iter 176 value 1.765977
iter 177 value 1.765790
iter 178 value 1.765451
iter 179 value 1.765213
iter 180 value 1.764695
iter 181 value 1.764525
iter 182 value 1.764209
iter 183 value 1.763926
iter 184 value 1.763870
iter 185 value 1.763787
iter 186 value 1.763753
iter 187 value 1.763697
iter 188 value 1.763528
iter 189 value 1.763385
iter 190 value 1.763303
iter 191 value 1.763168
iter 192 value 1.762831
iter 193 value 1.762695
iter 194 value 1.762602
iter 195 value 1.762552
iter 196 value 1.762455
iter 197 value 1.762343
iter 198 value 1.762168
iter 199 value 1.762135
iter 200 value 1.762016
iter 201 value 1.761980
iter 202 value 1.761934
iter 203 value 1.761894
iter 204 value 1.761790
iter 205 value 1.761753
iter 206 value 1.761609
iter 207 value 1.761482
iter 208 value 1.761445
iter 209 value 1.761418
iter 210 value 1.761400
iter 211 value 1.761351
iter 212 value 1.761149
iter 213 value 1.760962
iter 214 value 1.760890
iter 215 value 1.760782
iter 216 value 1.760719
iter 217 value 1.760595
iter 218 value 1.760512
iter 219 value 1.760473
iter 220 value 1.760382
iter 221 value 1.760313
iter 222 value 1.760294
iter 223 value 1.760248
iter 224 value 1.760152
iter 225 value 1.760117
iter 226 value 1.760066
iter 227 value 1.760057
iter 228 value 1.760040
iter 229 value 1.760025
iter 230 value 1.760009
iter 231 value 1.759980
iter 232 value 1.759924
iter 233 value 1.759908
iter 234 value 1.759879
iter 235 value 1.759858
iter 236 value 1.759847
iter 237 value 1.759840
iter 238 value 1.759834
iter 239 value 1.759829
iter 240 value 1.759823
iter 241 value 1.759821
iter 242 value 1.759801
iter 243 value 1.759774
iter 244 value 1.759732
iter 245 value 1.759710
iter 246 value 1.759683
iter 247 value 1.759662
iter 248 value 1.759657
iter 249 value 1.759648
iter 250 value 1.759641
iter 251 value 1.759631
final value 1.759631
stopped after 251 iterations
Final gradient value:
[1] -9.929488e-03 -5.935089e-03 -1.392935e-03 9.156231e-07 -1.147813e-02
[6] -7.498989e-03 9.867975e-04 4.033294e-03 -3.186218e-03 -5.441061e-03
[11] 6.096630e-04 1.712394e-01 -1.159265e-02 -2.709703e-03 1.164325e-03
[16] -1.196813e-02 -4.133826e-03 -1.374906e-02
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 1.76
-----------Population discrepancy function value, Fo
Point estimate : 1.161
Confidence Interval 90 % : ( 0.558 ; 2.02 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.254
Confidence Interval 90 % : ( 0.176 ; 0.335 )
-----------Discrepancy function TEST
TEST STATISTIC : 52.79
p values:
Ho: perfect fit (RMSEA=0.00) : 0
Ho: close fit (RMSEA=0.050) : 0
-----------Power estimation (alpha=0.05),
N 31
Degrees of freedom= 18
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.071
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.062
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.097
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.081
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 3.476
Confidence Interval 90 % : ( 2.872 ; 4.334 )
Hoelter's CN( .05 ) : 17
-----------Fit index
Chisquare (null model) = 158.6338 Df = 28
Bentler-Bonnett NFI : 0.667
Tucker-Lewis NNFI : 0.586
Bentler CFI : 0.734
SRMR : 0.254
GFI : 0.775
AGFI : 0.55
-----------Parsimony index
Akaike Information Criterion : 0.56
Bozdogans's Consistent AIC : -27.023
Schwarz's Bayesian Criterion : -0.301
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.04480 0.02973
a 2 0.01553 0.03432
v eventful 0.36413 0.29791
v vibrant 3124.05470 67106.23102
v pleasant 0.07616 0.15773
v calm 0.33665 0.19961
v uneventful 0.64675 0.42368
v monotonous 1.08850 0.69777
v annoying 0.49937 0.32880
v chaotic 0.43003 0.26072
z eventful 1.00048 0.18585
z vibrant 0.01789 0.19209
z pleasant 0.82652 0.12260
z calm 0.80897 0.12710
z uneventful 0.89066 0.18371
z monotonous 0.72705 0.17433
z annoying 0.73788 0.13983
z chaotic 0.79378 0.13689
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.86
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.02
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.96
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.86
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.78
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.69
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.82
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.84
(L ; U)
eventful ( 0.6 ; 0.97 )
vibrant ( 0 ; 1 )
pleasant ( 0.43 ; 1 )
calm ( 0.69 ; 0.95 )
uneventful ( 0.55 ; 0.92 )
monotonous ( 0.46 ; 0.87 )
annoying ( 0.6 ; 0.94 )
chaotic ( 0.64 ; 0.94 )
(MCSC) Correlation at 180 degrees: -0.886
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0423 0.9431 0.0146
----------------------------------------------------
CPU Time for optimization 0.51 sec. ( 0 min.)
circE.MYO.ea=CircE.BFGS(data.merged.myo.cor,
v.names = rownames(data.merged.mym.cor),
m=2,N=n.participsnts.MY.O,r=1, equal.ang = TRUE)Date: Wed Jul 5 14:09:40 2023
Data: Circumplex Estimation
Model:Equal spacing
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.06620263 0.08700006 Inf 0
a 2 0.00000000 8.78222153 Inf 0
v eventful 0.33051965 1.29320984 Inf 0
v vibrant 1.24510224 1.17090720 Inf 0
v pleasant 0.13929346 1.10475090 Inf 0
v calm 0.15159816 4.31501778 Inf 0
v uneventful 0.34422900 1.80101144 Inf 0
v monotonous 0.85626166 0.50124140 Inf 0
v annoying 0.39058767 0.27796144 Inf 0
v chaotic 0.42397242 0.60446297 Inf 0
z eventful 0.84830360 1.61873140 Inf 0
z vibrant 0.55540045 4.15690258 Inf 0
z pleasant 0.93277567 -0.70452562 Inf 0
z calm 0.92706956 1.64035291 Inf 0
z uneventful 0.84258910 2.13333956 Inf 0
z monotonous 0.65966327 1.60570768 Inf 0
z annoying 0.82359756 -0.10874380 Inf 0
z chaotic 0.81023595 0.69454238 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 2.558279
iter 2 value 1.759753
iter 3 value 1.707995
iter 4 value 1.693992
iter 5 value 1.677564
iter 6 value 1.665773
iter 7 value 1.662506
iter 8 value 1.656589
iter 9 value 1.647549
iter 10 value 1.632893
iter 11 value 1.603836
iter 12 value 1.554234
iter 13 value 1.515173
iter 14 value 1.492574
iter 15 value 1.453425
iter 16 value 1.433247
iter 17 value 1.417495
iter 18 value 1.411923
iter 19 value 1.409272
iter 20 value 1.400531
iter 21 value 1.389784
iter 22 value 1.371490
iter 23 value 1.355407
iter 24 value 1.351996
iter 25 value 1.345262
iter 26 value 1.339262
iter 27 value 1.334438
iter 28 value 1.325466
iter 29 value 1.318820
iter 30 value 1.314632
iter 31 value 1.312040
iter 32 value 1.307587
iter 33 value 1.301441
iter 34 value 1.293750
iter 35 value 1.285079
iter 36 value 1.278793
iter 37 value 1.271084
iter 38 value 1.266312
iter 39 value 1.260303
iter 40 value 1.257679
iter 41 value 1.256476
iter 42 value 1.255397
iter 43 value 1.254574
iter 44 value 1.253812
iter 45 value 1.252947
iter 46 value 1.252099
iter 47 value 1.250874
iter 48 value 1.249407
iter 49 value 1.248151
iter 50 value 1.246846
iter 51 value 1.245692
iter 52 value 1.244810
iter 53 value 1.244163
iter 54 value 1.243942
iter 55 value 1.243868
iter 56 value 1.243783
iter 57 value 1.243627
iter 58 value 1.243225
iter 59 value 1.242203
iter 60 value 1.240332
iter 61 value 1.239001
iter 62 value 1.236785
iter 63 value 1.235168
iter 64 value 1.234291
iter 65 value 1.233710
iter 66 value 1.233357
iter 67 value 1.232984
iter 68 value 1.232741
iter 69 value 1.232483
iter 70 value 1.231966
iter 71 value 1.230777
iter 72 value 1.228993
iter 73 value 1.227132
iter 74 value 1.224997
iter 75 value 1.217996
iter 76 value 1.211924
iter 77 value 1.206326
iter 78 value 1.204377
iter 79 value 1.202852
iter 80 value 1.202350
iter 81 value 1.201876
iter 82 value 1.201488
iter 83 value 1.201123
iter 84 value 1.200778
iter 85 value 1.200348
iter 86 value 1.199994
iter 87 value 1.199705
iter 88 value 1.199480
iter 89 value 1.199189
iter 90 value 1.198796
iter 91 value 1.198490
iter 92 value 1.197997
iter 93 value 1.197672
iter 94 value 1.197414
iter 95 value 1.197246
iter 96 value 1.197070
iter 97 value 1.196919
iter 98 value 1.196812
iter 99 value 1.196750
iter 100 value 1.196683
iter 101 value 1.196648
iter 102 value 1.196606
iter 103 value 1.196549
iter 104 value 1.196530
iter 105 value 1.196519
iter 106 value 1.196512
iter 107 value 1.196504
iter 108 value 1.196497
iter 109 value 1.196483
iter 110 value 1.196475
iter 111 value 1.196472
iter 112 value 1.196470
iter 113 value 1.196468
iter 114 value 1.196463
iter 115 value 1.196459
iter 116 value 1.196456
iter 117 value 1.196455
iter 118 value 1.196454
iter 119 value 1.196454
iter 120 value 1.196454
iter 121 value 1.196453
iter 122 value 1.196453
final value 1.196453
converged
Final gradient value:
[1] -6.649725e-04 -5.018636e-01 -5.302681e-05 1.544339e-06 -8.164022e-07
[6] -4.085874e-04 -1.039628e-04 -3.482420e-05 -1.490387e-04 1.070845e-05
[11] -5.458450e-04 2.116826e-03 2.760413e-04 -4.744922e-04 1.036361e-04
[16] 2.552700e-04 -3.561352e-04 -3.538721e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: ONE PARAMETER ( a 2 ) IS ON A BOUNDARY.
-----------Model degrees of freedom= 18
Active Bound= 1
The appropriate distribution for the test statistic lies between
chi-squared distribution with 18 and with 18 + 1 degrees of freedom.
-----------Values enclosed in square brackets are based on 18 + 1 = 19 degrees of freedom.
-----------Sample discrepancy function value : 1.196
-----------Population discrepancy function value, Fo
Point estimate : 0.616 [ 0.582 ]
Confidence Interval 90 % : ( 0.173 [ 0.147 ] ; 1.312 [ 1.275 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.185 [ 0.175 ]
Confidence Interval 90 % : ( 0.098 [ 0.088 ] ; 0.27 [ 0.259 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 37.09
p values:
Ho: perfect fit (RMSEA=0.00) : 0.005 [ 0.008 ]
Ho: close fit (RMSEA=0.050) : 0.011 [ 0.016 ]
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 18 [ 19 ]
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.071 [ 0.072 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.062 [ 0.062 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.099 [ 0.1 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.082 [ 0.083 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.833 [ 2.831 ]
Confidence Interval 90 % : ( 2.39 [ 2.396 ] ; 3.529 [ 3.524 ] )
Hoelter's CN( .05 ) : 25 [ 26 ]
-----------Fit index
Chisquare (null model) = 137.1038 Df = 28
Bentler-Bonnett NFI : 0.729
Tucker-Lewis NNFI : 0.728 [ 0.756 ]
Bentler CFI : 0.825 [ 0.825 ]
SRMR : 0.217
GFI : 0.867 [ 0.873 ]
AGFI : 0.734 [ 0.759 ]
-----------Parsimony index
Akaike Information Criterion : 0.035
Bozdogans's Consistent AIC : -43.293
Schwarz's Bayesian Criterion : -0.816
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.03336 0.02592
a 2 0.00000 0.02985
v eventful 0.13473 0.17611
v vibrant 83.02666 284.53650
v pleasant 0.22179 0.21143
v calm 0.37249 0.19372
v uneventful 0.17314 0.14965
v monotonous 1.63141 1.03999
v annoying 0.76944 0.49925
v chaotic 0.68945 0.36463
z eventful 1.20522 0.18244
z vibrant 0.10901 0.18606
z pleasant 0.77069 0.12511
z calm 0.88788 0.13618
z uneventful 1.18548 0.17198
z monotonous 0.60583 0.16109
z annoying 0.67191 0.14483
z chaotic 0.79433 0.14946
NOTE! ACTIVE BOUNDS FOR: a 2 ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.94
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.11
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.90
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.85
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.92
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.62
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.75
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.77
(L ; U)
eventful ( 0.6 ; 0.99 )
vibrant ( 0 ; 0.95 )
pleasant ( 0.64 ; 0.98 )
calm ( 0.7 ; 0.94 )
uneventful ( 0.72 ; 0.98 )
monotonous ( 0.39 ; 0.83 )
annoying ( 0.52 ; 0.91 )
chaotic ( 0.58 ; 0.9 )
(MCSC) Correlation at 180 degrees: -0.935
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0323 0.9677 0
----------------------------------------------------
CPU Time for optimization 0.237 sec. ( 0 min.)
circE.SG.ea=CircE.BFGS(data.merged.sg.cor,
v.names = rownames(data.merged.mym.cor),
m=2,N=n.participsnts.SG,r=1, equal.ang = TRUE)Date: Wed Jul 5 14:09:40 2023
Data: Circumplex Estimation
Model:Equal spacing
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.02019079 11.21780273 Inf 0
a 2 0.00000000 20.59179294 Inf 0
v eventful 0.29430282 2.76582931 Inf 0
v vibrant 0.71234795 2.30510030 Inf 0
v pleasant 0.10612477 2.67158267 Inf 0
v calm 0.10084613 10.03764467 Inf 0
v uneventful 0.36058846 2.68890873 Inf 0
v monotonous 1.35967739 0.59128961 Inf 0
v annoying 0.23341583 0.62986034 Inf 0
v chaotic 0.29767556 1.36338635 Inf 0
z eventful 0.86361738 2.54811651 Inf 0
z vibrant 0.70536260 3.15128286 Inf 0
z pleasant 0.94834443 -0.66964276 Inf 0
z calm 0.95084737 2.99546758 Inf 0
z uneventful 0.83582880 2.89587973 Inf 0
z monotonous 0.52936793 3.48483191 Inf 0
z annoying 0.89007942 -0.01599357 Inf 0
z chaotic 0.86217789 1.20862969 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 4.212549
iter 2 value 3.994985
iter 3 value 3.578270
iter 4 value 2.887797
iter 5 value 2.736871
iter 6 value 2.673630
iter 7 value 2.642253
iter 8 value 2.623043
iter 9 value 2.600339
iter 10 value 2.583147
iter 11 value 2.541715
iter 12 value 2.529712
iter 13 value 2.512055
iter 14 value 2.501320
iter 15 value 2.477537
iter 16 value 2.439790
iter 17 value 2.365766
iter 18 value 2.311859
iter 19 value 2.304111
iter 20 value 2.274282
iter 21 value 2.242070
iter 22 value 2.222357
iter 23 value 2.210288
iter 24 value 2.200056
iter 25 value 2.196009
iter 26 value 2.189996
iter 27 value 2.186965
iter 28 value 2.184404
iter 29 value 2.183543
iter 30 value 2.183187
iter 31 value 2.182488
iter 32 value 2.180423
iter 33 value 2.179858
iter 34 value 2.179407
iter 35 value 2.179332
iter 36 value 2.179161
iter 37 value 2.178881
iter 38 value 2.178623
iter 39 value 2.178331
iter 40 value 2.177985
iter 41 value 2.177879
iter 42 value 2.177434
iter 43 value 2.176402
iter 44 value 2.175118
iter 45 value 2.174163
iter 46 value 2.173272
iter 47 value 2.172852
iter 48 value 2.172511
iter 49 value 2.172275
iter 50 value 2.171523
iter 51 value 2.170610
iter 52 value 2.168925
iter 53 value 2.167965
iter 54 value 2.164185
iter 55 value 2.163618
iter 56 value 2.163361
iter 57 value 2.162833
iter 58 value 2.162176
iter 59 value 2.161992
iter 60 value 2.161002
iter 61 value 2.160817
iter 62 value 2.160731
iter 63 value 2.160583
iter 64 value 2.160506
iter 65 value 2.160382
iter 66 value 2.160318
iter 67 value 2.160206
iter 68 value 2.160153
iter 69 value 2.160046
iter 70 value 2.160003
iter 71 value 2.159878
iter 72 value 2.159813
iter 73 value 2.158703
iter 74 value 2.157405
iter 75 value 2.156341
iter 76 value 2.156129
iter 77 value 2.155460
iter 78 value 2.154845
iter 79 value 2.151870
iter 80 value 2.150344
iter 81 value 2.148736
iter 82 value 2.147714
iter 83 value 2.147352
iter 84 value 2.147323
iter 85 value 2.147308
iter 86 value 2.147303
iter 87 value 2.147285
iter 88 value 2.147268
iter 89 value 2.147152
iter 90 value 2.147107
iter 91 value 2.147064
iter 92 value 2.147037
iter 93 value 2.147016
iter 94 value 2.146991
iter 95 value 2.146949
iter 96 value 2.146915
iter 97 value 2.146863
iter 98 value 2.146815
iter 99 value 2.146744
iter 100 value 2.146325
iter 101 value 2.145538
iter 102 value 2.142934
iter 103 value 2.139212
iter 104 value 2.137066
iter 105 value 2.135708
iter 106 value 2.132027
iter 107 value 2.131660
iter 108 value 2.131396
iter 109 value 2.131211
iter 110 value 2.131160
iter 111 value 2.131067
iter 112 value 2.131030
iter 113 value 2.131014
iter 114 value 2.130973
iter 115 value 2.130945
iter 116 value 2.130909
iter 117 value 2.130882
iter 118 value 2.130865
iter 119 value 2.130841
iter 120 value 2.130826
iter 121 value 2.130819
iter 122 value 2.130817
iter 123 value 2.130817
iter 124 value 2.130814
iter 125 value 2.130814
iter 126 value 2.130813
iter 127 value 2.130813
final value 2.130813
converged
Final gradient value:
[1] -4.802858e-05 1.734684e-04 -1.136262e-03 6.006864e-05 -4.730216e-01
[6] -3.015430e-01 4.535498e-04 2.665697e-04 -6.326483e-05 9.078994e-05
[11] -1.295326e-03 2.378180e-03 -1.117675e-04 -1.222074e-04 -1.117985e-03
[16] -2.710626e-04 5.792081e-06 2.140254e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: 2 PARAMETERS ( v pleasant ; v calm ; ) ARE ON A BOUNDARY.
-----------Model degrees of freedom= 18
Active Bounds= 2
The appropriate distribution for the test statistic lies between
chi-squared distribution with 18 and with 18 + 2 degrees of freedom.
-----------Values enclosed in square brackets are based on 18 + 2 = 20 degrees of freedom.
-----------Sample discrepancy function value : 2.131
-----------Population discrepancy function value, Fo
Point estimate : 1.545 [ 1.491 ]
Confidence Interval 90 % : ( 0.871 [ 0.808 ] ; 2.478 [ 2.408 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.293 [ 0.273 ]
Confidence Interval 90 % : ( 0.22 [ 0.201 ] ; 0.371 [ 0.347 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 66.06
p values:
Ho: perfect fit (RMSEA=0.00) : 0 [ 0 ]
Ho: close fit (RMSEA=0.050) : 0 [ 0 ]
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 18 [ 20 ]
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.071 [ 0.073 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.062 [ 0.063 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.099 [ 0.102 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.082 [ 0.084 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 3.762 [ 3.772 ]
Confidence Interval 90 % : ( 3.088 [ 3.09 ] ; 4.695 [ 4.69 ] )
Hoelter's CN( .05 ) : 14 [ 16 ]
-----------Fit index
Chisquare (null model) = 167.7914 Df = 28
Bentler-Bonnett NFI : 0.606
Tucker-Lewis NNFI : 0.465 [ 0.539 ]
Bentler CFI : 0.656 [ 0.656 ]
SRMR : 0.231
GFI : 0.721 [ 0.728 ]
AGFI : 0.442 [ 0.51 ]
-----------Parsimony index
Akaike Information Criterion : 0.97
Bozdogans's Consistent AIC : -14.328
Schwarz's Bayesian Criterion : 0.118
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.08744 0.04363
a 2 0.09407 0.07723
v eventful 12.39277 19.35825
v vibrant 76.35831 254.94253
v pleasant 0.00000 0.21080
v calm 0.00000 0.23246
v uneventful 19.05775 33.16990
v monotonous 2.37543 1.98964
v annoying 0.02481 0.22180
v chaotic 0.26013 0.32067
z eventful 0.27450 0.20672
z vibrant 0.11417 0.18975
z pleasant 0.82409 0.14997
z calm 0.87190 0.16811
z uneventful 0.22702 0.19390
z monotonous 0.56366 0.20013
z annoying 0.87011 0.15445
z chaotic 0.81749 0.16629
NOTE! ACTIVE BOUNDS FOR: v pleasant ; v calm ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.27
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.11
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 1.00
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 1.00
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.22
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.54
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.99
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.89
(L ; U)
eventful ( 0.06 ; 0.8 )
vibrant ( 0 ; 0.95 )
pleasant ( NaN ; 1 )
calm ( NaN ; 1 )
uneventful ( 0.04 ; 0.78 )
monotonous ( 0.27 ; 0.83 )
annoying ( 0 ; 1 )
chaotic ( 0.51 ; 0.99 )
(MCSC) Correlation at 180 degrees: -0.693
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.074 0.8464 0.0796
----------------------------------------------------
CPU Time for optimization 0.246 sec. ( 0 min.)
circE.ARAUS.ea=CircE.BFGS(data.araus.cor,
v.names = rownames(data.araus.cor),
m=2,N=29,r=1, equal.ang = TRUE)Date: Wed Jul 5 14:09:40 2023
Data: Circumplex Estimation
Model:Equal spacing
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.02324581 1.4522756 Inf 0
a 2 0.00000000 6.0157063 Inf 0
v eventful 0.30627084 0.3259005 Inf 0
v vibrant 0.57251768 0.6251978 Inf 0
v pleasant 0.33043363 0.7215189 Inf 0
v calm 0.26541508 1.7084644 Inf 0
v uneventful 0.29951789 0.7729388 Inf 0
v monotonous 0.66121216 0.9276830 Inf 0
v annoying 0.40018524 0.7633254 Inf 0
v chaotic 0.52628193 0.7035983 Inf 0
z eventful 0.85852186 0.2863350 Inf 0
z vibrant 0.75390659 1.1157102 Inf 0
z pleasant 0.84833960 0.6469518 Inf 0
z calm 0.87605968 0.5442620 Inf 0
z uneventful 0.86139274 0.6556189 Inf 0
z monotonous 0.72262722 1.7100984 Inf 0
z annoying 0.81972945 0.8916314 Inf 0
z chaotic 0.77090119 0.7560998 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 1.327357
iter 2 value 1.268367
iter 3 value 1.078895
iter 4 value 1.005699
iter 5 value 0.969548
iter 6 value 0.962845
iter 7 value 0.958924
iter 8 value 0.932701
iter 9 value 0.926755
iter 10 value 0.911592
iter 11 value 0.890127
iter 12 value 0.859714
iter 13 value 0.851584
iter 14 value 0.849682
iter 15 value 0.847025
iter 16 value 0.841368
iter 17 value 0.837535
iter 18 value 0.832896
iter 19 value 0.829277
iter 20 value 0.826542
iter 21 value 0.825882
iter 22 value 0.825150
iter 23 value 0.824297
iter 24 value 0.822256
iter 25 value 0.820089
iter 26 value 0.819477
iter 27 value 0.818507
iter 28 value 0.818016
iter 29 value 0.817466
iter 30 value 0.816772
iter 31 value 0.816099
iter 32 value 0.814445
iter 33 value 0.813075
iter 34 value 0.812115
iter 35 value 0.811966
iter 36 value 0.811836
iter 37 value 0.811688
iter 38 value 0.811542
iter 39 value 0.811398
iter 40 value 0.811276
iter 41 value 0.811182
iter 42 value 0.811101
iter 43 value 0.810968
iter 44 value 0.810790
iter 45 value 0.810770
iter 46 value 0.810662
iter 47 value 0.810633
iter 48 value 0.810630
iter 49 value 0.810625
iter 50 value 0.810617
iter 51 value 0.810612
iter 52 value 0.810608
iter 53 value 0.810604
iter 54 value 0.810603
iter 55 value 0.810601
iter 56 value 0.810600
iter 57 value 0.810599
iter 58 value 0.810599
iter 59 value 0.810599
final value 0.810599
converged
Final gradient value:
[1] 1.479789e-04 -4.038458e-01 6.384158e-05 -2.370502e-04 1.798666e-04
[6] 3.872255e-05 8.605022e-05 1.715131e-05 -3.775692e-05 6.622207e-05
[11] -1.273757e-05 1.177121e-04 7.660608e-05 -6.849780e-05 -6.013688e-05
[16] 2.726848e-05 -3.564494e-05 1.008626e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: ONE PARAMETER ( a 2 ) IS ON A BOUNDARY.
-----------Model degrees of freedom= 18
Active Bound= 1
The appropriate distribution for the test statistic lies between
chi-squared distribution with 18 and with 18 + 1 degrees of freedom.
-----------Values enclosed in square brackets are based on 18 + 1 = 19 degrees of freedom.
-----------Sample discrepancy function value : 0.811
-----------Population discrepancy function value, Fo
Point estimate : 0.169 [ 0.131 ]
Confidence Interval 90 % : ( 0 [ 0 ] ; 0.749 [ 0.708 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.097 [ 0.083 ]
Confidence Interval 90 % : ( 0 [ 0 ] ; 0.204 [ 0.193 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 22.7
p values:
Ho: perfect fit (RMSEA=0.00) : 0.202 [ 0.251 ]
Ho: close fit (RMSEA=0.050) : 0.269 [ 0.325 ]
-----------Power estimation (alpha=0.05),
N 29
Degrees of freedom= 18 [ 19 ]
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.069 [ 0.07 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.061 [ 0.061 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.094 [ 0.095 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.079 [ 0.08 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.707 [ 2.704 ]
Confidence Interval 90 % : ( 2.538 [ 2.573 ] ; 3.287 [ 3.281 ] )
Hoelter's CN( .05 ) : 36 [ 38 ]
-----------Fit index
Chisquare (null model) = 120.3928 Df = 28
Bentler-Bonnett NFI : 0.811
Tucker-Lewis NNFI : 0.921 [ 0.941 ]
Bentler CFI : 0.949 [ 0.949 ]
SRMR : 0.159
GFI : 0.959 [ 0.968 ]
AGFI : 0.918 [ 0.939 ]
-----------Parsimony index
Akaike Information Criterion : -0.475
Bozdogans's Consistent AIC : -55.915
Schwarz's Bayesian Criterion : -1.354
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.00461 0.02687
a 2 0.00000 0.03753
v eventful 0.29558 0.24260
v vibrant 0.90827 0.54331
v pleasant 0.39071 0.29418
v calm 0.83295 0.49145
v uneventful 0.38210 0.28715
v monotonous 1.72968 1.18304
v annoying 0.53343 0.38602
v chaotic 1.14953 0.71052
z eventful 0.86279 0.15094
z vibrant 0.74930 0.16668
z pleasant 0.86138 0.16101
z calm 0.71376 0.15365
z uneventful 0.83805 0.15512
z monotonous 0.62072 0.17840
z annoying 0.82009 0.16787
z chaotic 0.66208 0.16067
NOTE! ACTIVE BOUNDS FOR: a 2 ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.88
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.72
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.85
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.74
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.85
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.61
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.81
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.68
(L ; U)
eventful ( 0.64 ; 0.97 )
vibrant ( 0.5 ; 0.88 )
pleasant ( 0.61 ; 0.96 )
calm ( 0.52 ; 0.89 )
uneventful ( 0.61 ; 0.96 )
monotonous ( 0.36 ; 0.83 )
annoying ( 0.56 ; 0.94 )
chaotic ( 0.45 ; 0.86 )
(MCSC) Correlation at 180 degrees: -0.991
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0046 0.9954 0
----------------------------------------------------
CPU Time for optimization 0.119 sec. ( 0 min.)
#quasi-circumplex
circE.MYM.q=CircE.BFGS(data.merged.mym.cor,
v.names = rownames(data.merged.mym.cor),
m=2,N=n.participsnts.MY.M,r=1)Date: Wed Jul 5 14:09:41 2023
Data: Circumplex Estimation
Model:Unconstrained model
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 0.4866015 0.042008901 Inf -Inf
pleasant 4.4649237 -0.037242812 Inf -Inf
calm 4.3918000 -0.002816008 Inf -Inf
uneventful 2.9755937 -0.009234562 Inf -Inf
monotonous 2.1126030 0.002673717 Inf -Inf
annoying 1.3420145 -0.039025515 Inf -Inf
chaotic 0.9691427 -0.001812710 Inf -Inf
a 0 0.0380179 2.458383659 Inf 0
a 2 0.0000000 7.549641128 Inf 0
v eventful 0.4388344 0.326173469 Inf 0
v vibrant 0.4114001 0.499638171 Inf 0
v pleasant 0.1041365 -0.401269140 Inf 0
v calm 0.1158413 0.149808825 Inf 0
v uneventful 0.6357560 0.367813110 Inf 0
v monotonous 0.8890249 0.471027463 Inf 0
v annoying 0.3290793 0.527644649 Inf 0
v chaotic 0.2566440 0.652255759 Inf 0
z eventful 0.8043718 0.390198641 Inf 0
z vibrant 0.8152370 0.495552462 Inf 0
z pleasant 0.9492864 0.005489046 Inf 0
z calm 0.9437553 0.081127959 Inf 0
z uneventful 0.7314296 0.720339521 Inf 0
z monotonous 0.6498327 1.414747312 Inf 0
z annoying 0.8489066 0.412948170 Inf 0
z chaotic 0.8798776 0.355028232 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 0.218381
iter 2 value 0.184675
iter 3 value 0.124903
iter 4 value 0.105513
iter 5 value 0.085070
iter 6 value 0.082394
iter 7 value 0.081216
iter 8 value 0.080210
iter 9 value 0.078783
iter 10 value 0.076848
iter 11 value 0.075209
iter 12 value 0.073569
iter 13 value 0.071693
iter 14 value 0.070070
iter 15 value 0.066298
iter 16 value 0.064947
iter 17 value 0.063252
iter 18 value 0.062502
iter 19 value 0.061626
iter 20 value 0.060374
iter 21 value 0.059002
iter 22 value 0.058334
iter 23 value 0.057993
iter 24 value 0.057606
iter 25 value 0.057251
iter 26 value 0.057014
iter 27 value 0.056955
iter 28 value 0.056918
iter 29 value 0.056863
iter 30 value 0.056798
iter 31 value 0.056688
iter 32 value 0.056597
iter 33 value 0.056565
iter 34 value 0.056546
iter 35 value 0.056534
iter 36 value 0.056516
iter 37 value 0.056500
iter 38 value 0.056495
iter 39 value 0.056489
iter 40 value 0.056484
iter 41 value 0.056480
iter 42 value 0.056475
iter 43 value 0.056474
iter 44 value 0.056472
iter 45 value 0.056471
iter 46 value 0.056470
iter 47 value 0.056469
iter 48 value 0.056469
iter 49 value 0.056468
iter 50 value 0.056468
final value 0.056468
converged
Final gradient value:
[1] 7.942427e-05 -8.056943e-05 -1.932302e-04 -3.175744e-04 6.815749e-05
[6] 4.369285e-04 8.564913e-04 3.699884e-04 1.242849e-04 -2.175161e-04
[11] -5.867822e-05 -4.594076e-04 -2.610308e-04 -7.501195e-04 5.648067e-05
[16] -1.182552e-04 -1.638569e-04 -8.136165e-05 3.290033e-05 1.750543e-04
[21] -2.955869e-05 2.549981e-04 4.379288e-05 1.296555e-04 1.859799e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.056
-----------Population discrepancy function value, Fo
Point estimate : 0
Confidence Interval 90 % : ( 0 ; 0 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0
Confidence Interval 90 % : ( 0.001 ; 0.001 )
-----------Discrepancy function TEST
TEST STATISTIC : 1.69
p values:
Ho: perfect fit (RMSEA=0.00) : 0.999
Ho: close fit (RMSEA=0.050) : 1
-----------Power estimation (alpha=0.05),
N 31
Degrees of freedom= 11
Effective number of parameters= 25
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.065
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.059
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.086
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.074
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.748
Confidence Interval 90 % : ( 2.748 ; 2.748 )
Hoelter's CN( .05 ) : 344
-----------Fit index
Chisquare (null model) = 158.6338 Df = 28
Bentler-Bonnett NFI : 0.989
Tucker-Lewis NNFI : 1.181
Bentler CFI : 1
SRMR : 0.016
GFI : 1
AGFI : 1
-----------Parsimony index
Akaike Information Criterion : -1.61
Bozdogans's Consistent AIC : -109.156
Schwarz's Bayesian Criterion : -2.805
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 29.35956 12.52960
pleasant 256.04997 13.37512
calm 251.24387 13.64388
uneventful 172.13321 12.84587
monotonous 121.33559 18.22209
annoying 77.21959 14.27882
chaotic 56.43418 12.94529
a 0 0.03967 0.02562
a 2 0.02653 0.02784
v eventful 0.37121 0.32610
v vibrant 0.55741 0.31556
v pleasant 0.06290 0.06840
v calm 0.13809 0.08507
v uneventful 0.68825 0.60045
v monotonous 1.51744 1.07363
v annoying 0.34680 0.20341
v chaotic 0.29627 0.16473
z eventful 0.85855 0.17084
z vibrant 0.80498 0.15686
z pleasant 0.96564 0.13289
z calm 0.93443 0.13610
z uneventful 0.77232 0.19473
z monotonous 0.62841 0.17930
z annoying 0.85840 0.14824
z chaotic 0.87875 0.14545
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.85
vibrant 29 ( 5 ; 54 ) 331 ( 355 ; 306 ) 0.80
chaotic 56 ( 31 ; 82 ) 304 ( 329 ; 278 ) 0.88
annoying 77 ( 49 ; 105 ) 283 ( 311 ; 255 ) 0.86
monotonous 121 ( 86 ; 157 ) 239 ( 274 ; 203 ) 0.63
uneventful 172 ( 147 ; 197 ) 188 ( 213 ; 163 ) 0.77
calm 251 ( 225 ; 278 ) 109 ( 135 ; 82 ) 0.94
pleasant 256 ( 230 ; 282 ) 104 ( 130 ; 78 ) 0.97
(L ; U)
eventful ( 0.57 ; 0.97 )
vibrant ( 0.61 ; 0.92 )
chaotic ( 0.73 ; 0.95 )
annoying ( 0.69 ; 0.95 )
monotonous ( 0.38 ; 0.85 )
uneventful ( 0.46 ; 0.94 )
calm ( 0.83 ; 0.98 )
pleasant ( 0.81 ; 1 )
(MCSC) Correlation at 180 degrees: -0.876
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0372 0.9379 0.0249
----------------------------------------------------
CPU Time for optimization 0.139 sec. ( 0 min.)
circE.MYO.q=CircE.BFGS(data.merged.myo.cor,
v.names = rownames(data.merged.myo.cor),
m=2,N=n.participsnts.MY.O,r=1)Date: Wed Jul 5 14:09:41 2023
Data: Circumplex Estimation
Model:Unconstrained model
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 0.45384745 -0.020867578 Inf -Inf
pleasant 4.79117045 -0.068774392 Inf -Inf
calm 4.68607733 -0.066870034 Inf -Inf
uneventful 3.06659201 0.016983518 Inf -Inf
monotonous 2.25528126 0.017423799 Inf -Inf
annoying 1.65637071 0.029366927 Inf -Inf
chaotic 1.31797429 0.046487239 Inf -Inf
a 0 0.06620263 -0.071268263 Inf 0
a 2 0.00000000 4.598704639 Inf 0
v eventful 0.33051965 0.165929546 Inf 0
v vibrant 1.24510224 0.650524644 Inf 0
v pleasant 0.13929346 -0.183078882 Inf 0
v calm 0.15159816 -0.061553581 Inf 0
v uneventful 0.34422900 0.162212758 Inf 0
v monotonous 0.85626166 0.515460716 Inf 0
v annoying 0.39058767 0.395511740 Inf 0
v chaotic 0.42397242 0.465149342 Inf 0
z eventful 0.84830360 0.168534990 Inf 0
z vibrant 0.55540045 2.864080124 Inf 0
z pleasant 0.93277567 -0.022114984 Inf 0
z calm 0.92706956 0.002082038 Inf 0
z uneventful 0.84258910 0.102853626 Inf 0
z monotonous 0.65966327 1.394562477 Inf 0
z annoying 0.82359756 0.408772366 Inf 0
z chaotic 0.81023595 0.504614249 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 0.444274
iter 2 value 0.373180
iter 3 value 0.243021
iter 4 value 0.219415
iter 5 value 0.208444
iter 6 value 0.202585
iter 7 value 0.200728
iter 8 value 0.191523
iter 9 value 0.185283
iter 10 value 0.176294
iter 11 value 0.170941
iter 12 value 0.168111
iter 13 value 0.157902
iter 14 value 0.153775
iter 15 value 0.145906
iter 16 value 0.140120
iter 17 value 0.132327
iter 18 value 0.124685
iter 19 value 0.118437
iter 20 value 0.116047
iter 21 value 0.113431
iter 22 value 0.111617
iter 23 value 0.107836
iter 24 value 0.102522
iter 25 value 0.096723
iter 26 value 0.092855
iter 27 value 0.089825
iter 28 value 0.086715
iter 29 value 0.084800
iter 30 value 0.083577
iter 31 value 0.081947
iter 32 value 0.081619
iter 33 value 0.080962
iter 34 value 0.080625
iter 35 value 0.080300
iter 36 value 0.079966
iter 37 value 0.079744
iter 38 value 0.079528
iter 39 value 0.079357
iter 40 value 0.078968
iter 41 value 0.078789
iter 42 value 0.078632
iter 43 value 0.078497
iter 44 value 0.078382
iter 45 value 0.077882
iter 46 value 0.077577
iter 47 value 0.077035
iter 48 value 0.076402
iter 49 value 0.075963
iter 50 value 0.075740
iter 51 value 0.075308
iter 52 value 0.075172
iter 53 value 0.075044
iter 54 value 0.074811
iter 55 value 0.074312
iter 56 value 0.073603
iter 57 value 0.073082
iter 58 value 0.072848
iter 59 value 0.072750
iter 60 value 0.072712
iter 61 value 0.072666
iter 62 value 0.072646
iter 63 value 0.072626
iter 64 value 0.072601
iter 65 value 0.072591
iter 66 value 0.072567
iter 67 value 0.072563
iter 68 value 0.072559
iter 69 value 0.072559
iter 70 value 0.072552
iter 71 value 0.072551
iter 72 value 0.072549
iter 73 value 0.072547
iter 74 value 0.072546
iter 75 value 0.072544
iter 76 value 0.072543
iter 77 value 0.072543
final value 0.072543
converged
Final gradient value:
[1] 5.888004e-05 2.326738e-06 -3.849435e-04 -1.479530e-04 -5.435354e-04
[6] 4.345890e-05 2.872931e-04 -1.104142e-03 -2.823752e-03 -1.203758e-04
[11] -4.827093e-05 -4.185671e-04 -1.809268e-04 -3.509475e-04 5.183775e-06
[16] -1.109408e-04 5.308964e-05 -5.839929e-04 -3.128481e-04 -1.052629e-04
[21] -4.732545e-04 -2.238476e-04 4.508974e-04 -1.005229e-04 1.190792e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.073
-----------Population discrepancy function value, Fo
Point estimate : 0
Confidence Interval 90 % : ( 0 ; 0 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0
Confidence Interval 90 % : ( 0.001 ; 0.001 )
-----------Discrepancy function TEST
TEST STATISTIC : 2.25
p values:
Ho: perfect fit (RMSEA=0.00) : 0.997
Ho: close fit (RMSEA=0.050) : 0.998
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 11
Effective number of parameters= 25
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.065
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.059
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.087
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.075
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.628
Confidence Interval 90 % : ( 2.628 ; 2.628 )
Hoelter's CN( .05 ) : 268
-----------Fit index
Chisquare (null model) = 137.1038 Df = 28
Bentler-Bonnett NFI : 0.984
Tucker-Lewis NNFI : 1.204
Bentler CFI : 1
SRMR : 0.02
GFI : 1
AGFI : 1
-----------Parsimony index
Akaike Information Criterion : -1.54
Bozdogans's Consistent AIC : -109.395
Schwarz's Bayesian Criterion : -2.722
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 25.99823 22.13474
pleasant 272.59492 12.08945
calm 267.26258 12.33370
uneventful 176.50080 8.73763
monotonous 128.62882 16.77769
annoying 94.89389 13.41985
chaotic 76.76282 13.76255
a 0 0.05694 0.02990
a 2 0.02019 0.02472
v eventful 0.13049 0.26519
v vibrant 3.83119 3.23507
v pleasant 0.11728 0.09592
v calm 0.15445 0.10638
v uneventful 0.22527 0.30970
v monotonous 1.52533 0.97815
v annoying 0.41175 0.25439
v chaotic 0.56872 0.32896
z eventful 0.94590 0.17913
z vibrant 0.45564 0.17786
z pleasant 0.94069 0.13492
z calm 0.92618 0.13646
z uneventful 0.90799 0.18182
z monotonous 0.62788 0.16876
z annoying 0.83721 0.15121
z chaotic 0.79620 0.15496
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.94
vibrant 26 ( 343 ; 69 ) 334 ( 17 ; 291 ) 0.45
chaotic 77 ( 50 ; 104 ) 283 ( 310 ; 256 ) 0.80
annoying 95 ( 69 ; 121 ) 265 ( 291 ; 239 ) 0.84
monotonous 129 ( 96 ; 162 ) 231 ( 264 ; 198 ) 0.63
uneventful 177 ( 159 ; 194 ) 183 ( 201 ; 166 ) 0.90
calm 267 ( 243 ; 291 ) 93 ( 117 ; 69 ) 0.93
pleasant 273 ( 249 ; 296 ) 87 ( 111 ; 64 ) 0.95
(L ; U)
eventful ( 0.35 ; 1 )
vibrant ( 0.22 ; 0.76 )
chaotic ( 0.6 ; 0.92 )
annoying ( 0.65 ; 0.94 )
monotonous ( 0.4 ; 0.84 )
uneventful ( 0.48 ; 0.99 )
calm ( 0.79 ; 0.98 )
pleasant ( 0.79 ; 0.99 )
(MCSC) Correlation at 180 degrees: -0.857
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0529 0.9284 0.0187
----------------------------------------------------
CPU Time for optimization 0.207 sec. ( 0 min.)
circE.SG.q=CircE.BFGS(data.merged.sg.cor,
v.names = rownames(data.merged.sg.cor),
m=2,N=n.participsnts.SG,r=1)Date: Wed Jul 5 14:09:41 2023
Data: Circumplex Estimation
Model:Unconstrained model
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 0.55278045 0.003228475 Inf -Inf
pleasant 5.00053540 -0.189144157 Inf -Inf
calm 4.91712634 -0.161755316 Inf -Inf
uneventful 3.10703919 0.051391937 Inf -Inf
monotonous 2.68269456 -0.013701742 Inf -Inf
annoying 1.77776315 0.111708934 Inf -Inf
chaotic 1.38051075 0.114918943 Inf -Inf
a 0 0.02019079 8.399667273 Inf 0
a 2 0.00000000 10.424621075 Inf 0
v eventful 0.29430282 0.407001454 Inf 0
v vibrant 0.71234795 0.630672070 Inf 0
v pleasant 0.10612477 0.465048057 Inf 0
v calm 0.10084613 -0.157532976 Inf 0
v uneventful 0.36058846 0.487787052 Inf 0
v monotonous 1.35967739 0.609945209 Inf 0
v annoying 0.23341583 0.374115741 Inf 0
v chaotic 0.29767556 1.071806342 Inf 0
z eventful 0.86361738 0.353019662 Inf 0
z vibrant 0.70536260 1.248327077 Inf 0
z pleasant 0.94834443 0.198215080 Inf 0
z calm 0.95084737 0.121062282 Inf 0
z uneventful 0.83582880 0.404969681 Inf 0
z monotonous 0.52936793 3.240265394 Inf 0
z annoying 0.89007942 0.303792799 Inf 0
z chaotic 0.86217789 0.615897347 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 0.634597
iter 2 value 0.432457
iter 3 value 0.408826
iter 4 value 0.366249
iter 5 value 0.326854
iter 6 value 0.295882
iter 7 value 0.286380
iter 8 value 0.276062
iter 9 value 0.267249
iter 10 value 0.262335
iter 11 value 0.250710
iter 12 value 0.245854
iter 13 value 0.234613
iter 14 value 0.228401
iter 15 value 0.224507
iter 16 value 0.220314
iter 17 value 0.215982
iter 18 value 0.210619
iter 19 value 0.203187
iter 20 value 0.191760
iter 21 value 0.184533
iter 22 value 0.181883
iter 23 value 0.179083
iter 24 value 0.175256
iter 25 value 0.169706
iter 26 value 0.164596
iter 27 value 0.160958
iter 28 value 0.159041
iter 29 value 0.157541
iter 30 value 0.155958
iter 31 value 0.154605
iter 32 value 0.154310
iter 33 value 0.153806
iter 34 value 0.153593
iter 35 value 0.153303
iter 36 value 0.153139
iter 37 value 0.152986
iter 38 value 0.152923
iter 39 value 0.152760
iter 40 value 0.152545
iter 41 value 0.152312
iter 42 value 0.151931
iter 43 value 0.151629
iter 44 value 0.151298
iter 45 value 0.151086
iter 46 value 0.150827
iter 47 value 0.150617
iter 48 value 0.150252
iter 49 value 0.149560
iter 50 value 0.149110
iter 51 value 0.148155
iter 52 value 0.147606
iter 53 value 0.147338
iter 54 value 0.147159
iter 55 value 0.146675
iter 56 value 0.145988
iter 57 value 0.145681
iter 58 value 0.145475
iter 59 value 0.145415
iter 60 value 0.145387
iter 61 value 0.145362
iter 62 value 0.145317
iter 63 value 0.145306
iter 64 value 0.145293
iter 65 value 0.145280
iter 66 value 0.145260
iter 67 value 0.145232
iter 68 value 0.145228
iter 69 value 0.145215
iter 70 value 0.145213
iter 71 value 0.145211
iter 72 value 0.145210
iter 73 value 0.145209
iter 74 value 0.145208
iter 75 value 0.145207
iter 76 value 0.145206
iter 77 value 0.145206
final value 0.145206
converged
Final gradient value:
[1] 1.610480e-04 -3.712613e-04 2.027300e-04 3.998766e-05 2.325109e-05
[6] 2.600325e-04 1.195456e-06 3.416580e-04 1.446771e-03 -1.465237e-05
[11] -4.702547e-05 1.954045e-05 -2.480085e-04 1.315206e-04 -1.720666e-05
[16] 3.400575e-04 -1.575061e-04 1.033298e-04 -5.333925e-05 1.809062e-04
[21] -2.908592e-04 2.662791e-05 -3.889385e-04 3.280449e-04 -1.625054e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.145
-----------Population discrepancy function value, Fo
Point estimate : 0
Confidence Interval 90 % : ( 0 ; 0 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0
Confidence Interval 90 % : ( 0.001 ; 0.001 )
-----------Discrepancy function TEST
TEST STATISTIC : 4.5
p values:
Ho: perfect fit (RMSEA=0.00) : 0.953
Ho: close fit (RMSEA=0.050) : 0.965
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 11
Effective number of parameters= 25
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.065
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.059
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.087
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.075
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.628
Confidence Interval 90 % : ( 2.628 ; 2.628 )
Hoelter's CN( .05 ) : 135
-----------Fit index
Chisquare (null model) = 167.7914 Df = 28
Bentler-Bonnett NFI : 0.973
Tucker-Lewis NNFI : 1.118
Bentler CFI : 1
SRMR : 0.037
GFI : 1
AGFI : 1
-----------Parsimony index
Akaike Information Criterion : -1.468
Bozdogans's Consistent AIC : -107.142
Schwarz's Bayesian Criterion : -2.65
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 34.14764 15.09484
pleasant 282.04605 11.49691
calm 277.93356 11.36138
uneventful 178.35150 8.90533
monotonous 146.66851 21.42629
annoying 100.80494 11.72137
chaotic 80.93917 13.07572
a 0 0.03911 0.02181
a 2 0.02158 0.01945
v eventful 0.10030 0.20881
v vibrant 1.65339 1.06276
v pleasant 0.12206 0.07202
v calm 0.06200 0.05857
v uneventful 0.34921 0.32898
v monotonous 3.39778 2.74304
v annoying 0.14748 0.11886
v chaotic 0.46429 0.24012
z eventful 0.94444 0.16123
z vibrant 0.61479 0.16855
z pleasant 0.94923 0.13384
z calm 0.97744 0.13122
z uneventful 0.85456 0.17336
z monotonous 0.47563 0.17608
z annoying 0.93857 0.13973
z chaotic 0.83337 0.14939
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.95
vibrant 34 ( 5 ; 64 ) 326 ( 355 ; 296 ) 0.61
chaotic 81 ( 55 ; 107 ) 279 ( 305 ; 253 ) 0.83
annoying 101 ( 78 ; 124 ) 259 ( 282 ; 236 ) 0.93
monotonous 147 ( 105 ; 189 ) 213 ( 255 ; 171 ) 0.48
uneventful 178 ( 161 ; 196 ) 182 ( 199 ; 164 ) 0.86
calm 278 ( 256 ; 300 ) 82 ( 104 ; 60 ) 0.97
pleasant 282 ( 260 ; 305 ) 78 ( 100 ; 55 ) 0.94
(L ; U)
eventful ( 0.38 ; 1 )
vibrant ( 0.38 ; 0.83 )
chaotic ( 0.66 ; 0.93 )
annoying ( 0.76 ; 0.99 )
monotonous ( 0.24 ; 0.77 )
uneventful ( 0.56 ; 0.97 )
calm ( 0.85 ; 1 )
pleasant ( 0.85 ; 0.98 )
(MCSC) Correlation at 180 degrees: -0.886
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0369 0.9428 0.0203
----------------------------------------------------
CPU Time for optimization 0.21 sec. ( 0 min.)
circE.ARAUS.q=CircE.BFGS(data.araus.cor,
v.names = rownames(data.araus.cor),
m=2,N=29,r=1)Date: Wed Jul 5 14:09:42 2023
Data: Circumplex Estimation
Model:Unconstrained model
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 5.79194185 0.006740875 Inf -Inf
pleasant 4.48269171 -0.001883151 Inf -Inf
calm 4.28559422 0.004154045 Inf -Inf
uneventful 3.06502168 -0.012784974 Inf -Inf
monotonous 2.82071606 0.013784137 Inf -Inf
annoying 1.29014691 -0.002225632 Inf -Inf
chaotic 0.75010314 -0.033305375 Inf -Inf
a 0 0.02324581 0.363731168 Inf 0
a 2 0.00000000 6.809648702 Inf 0
v eventful 0.30627084 0.369161657 Inf 0
v vibrant 0.57251768 0.466606506 Inf 0
v pleasant 0.33043363 0.286720598 Inf 0
v calm 0.26541508 0.254780413 Inf 0
v uneventful 0.29951789 0.245654097 Inf 0
v monotonous 0.66121216 0.619186222 Inf 0
v annoying 0.40018524 0.352534504 Inf 0
v chaotic 0.52628193 0.718001151 Inf 0
z eventful 0.85852186 0.280189237 Inf 0
z vibrant 0.75390659 0.741044759 Inf 0
z pleasant 0.84833960 0.271816701 Inf 0
z calm 0.87605968 0.190900446 Inf 0
z uneventful 0.86139274 0.222683563 Inf 0
z monotonous 0.72262722 1.063826428 Inf 0
z annoying 0.81972945 0.412260131 Inf 0
z chaotic 0.77090119 0.865538137 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 0.336524
iter 2 value 0.300494
iter 3 value 0.253001
iter 4 value 0.229414
iter 5 value 0.195231
iter 6 value 0.180488
iter 7 value 0.172316
iter 8 value 0.162638
iter 9 value 0.153228
iter 10 value 0.147839
iter 11 value 0.141288
iter 12 value 0.127817
iter 13 value 0.114406
iter 14 value 0.110917
iter 15 value 0.109082
iter 16 value 0.107777
iter 17 value 0.104493
iter 18 value 0.100899
iter 19 value 0.098310
iter 20 value 0.096624
iter 21 value 0.095727
iter 22 value 0.095065
iter 23 value 0.094005
iter 24 value 0.093692
iter 25 value 0.093562
iter 26 value 0.093459
iter 27 value 0.093286
iter 28 value 0.093169
iter 29 value 0.093099
iter 30 value 0.093048
iter 31 value 0.093005
iter 32 value 0.092994
iter 33 value 0.092924
iter 34 value 0.092865
iter 35 value 0.092817
iter 36 value 0.092789
iter 37 value 0.092764
iter 38 value 0.092756
iter 39 value 0.092747
iter 40 value 0.092739
iter 41 value 0.092727
iter 42 value 0.092721
iter 43 value 0.092710
iter 44 value 0.092707
iter 45 value 0.092706
iter 46 value 0.092705
iter 47 value 0.092704
iter 48 value 0.092703
iter 49 value 0.092702
iter 50 value 0.092702
iter 51 value 0.092702
final value 0.092702
converged
Final gradient value:
[1] 1.695336e-04 -1.066794e-04 -1.196530e-04 -1.348725e-04 9.574616e-06
[6] 5.042995e-06 -1.891354e-05 3.238377e-04 -4.140119e-04 -6.353524e-06
[11] 1.557292e-04 -4.036366e-05 1.280930e-04 1.184476e-04 -3.725526e-05
[16] -7.715454e-05 -9.471509e-06 2.005944e-05 -5.971350e-05 2.382834e-05
[21] 7.861905e-05 8.532164e-05 6.375402e-06 -2.047589e-05 -8.044891e-06
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.093
-----------Population discrepancy function value, Fo
Point estimate : 0
Confidence Interval 90 % : ( 0 ; 0 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0
Confidence Interval 90 % : ( 0.001 ; 0.001 )
-----------Discrepancy function TEST
TEST STATISTIC : 2.6
p values:
Ho: perfect fit (RMSEA=0.00) : 0.995
Ho: close fit (RMSEA=0.050) : 0.996
-----------Power estimation (alpha=0.05),
N 29
Degrees of freedom= 11
Effective number of parameters= 25
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.064
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.059
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.084
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.073
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 3.024
Confidence Interval 90 % : ( 3.024 ; 3.024 )
Hoelter's CN( .05 ) : 210
-----------Fit index
Chisquare (null model) = 120.3928 Df = 28
Bentler-Bonnett NFI : 0.978
Tucker-Lewis NNFI : 1.232
Bentler CFI : 1
SRMR : 0.023
GFI : 1
AGFI : 1
-----------Parsimony index
Akaike Information Criterion : -1.693
Bozdogans's Consistent AIC : -106.587
Schwarz's Bayesian Criterion : -2.914
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 331.97590 13.13633
pleasant 257.03391 14.01842
calm 246.81333 13.05895
uneventful 176.39284 9.16794
monotonous 165.52680 15.34491
annoying 74.98817 13.82546
chaotic 42.29889 14.69287
a 0 0.01460 0.02264
a 2 0.02198 0.02807
v eventful 0.26141 0.20218
v vibrant 0.89430 0.55632
v pleasant 0.36789 0.22601
v calm 0.25146 0.17288
v uneventful 0.21723 0.20650
v monotonous 1.35233 0.87672
v annoying 0.34418 0.28681
v chaotic 0.97013 0.58223
z eventful 0.88863 0.15654
z vibrant 0.72438 0.16997
z pleasant 0.85780 0.15622
z calm 0.89780 0.15271
z uneventful 0.90416 0.15988
z monotonous 0.65075 0.17406
z annoying 0.86557 0.16953
z chaotic 0.71433 0.16925
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.89
chaotic 42 ( 14 ; 71 ) 318 ( 346 ; 289 ) 0.71
annoying 75 ( 48 ; 102 ) 285 ( 312 ; 258 ) 0.86
monotonous 166 ( 135 ; 196 ) 194 ( 225 ; 164 ) 0.65
uneventful 176 ( 158 ; 194 ) 184 ( 202 ; 166 ) 0.91
calm 247 ( 221 ; 272 ) 113 ( 139 ; 88 ) 0.89
pleasant 257 ( 230 ; 285 ) 103 ( 130 ; 75 ) 0.86
vibrant 332 ( 306 ; 358 ) 28 ( 54 ; 2 ) 0.73
(L ; U)
eventful ( 0.68 ; 0.97 )
chaotic ( 0.49 ; 0.88 )
annoying ( 0.6 ; 0.97 )
monotonous ( 0.41 ; 0.85 )
uneventful ( 0.65 ; 0.98 )
calm ( 0.71 ; 0.97 )
pleasant ( 0.67 ; 0.95 )
vibrant ( 0.5 ; 0.89 )
(MCSC) Correlation at 180 degrees: -0.929
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0141 0.9647 0.0212
----------------------------------------------------
CPU Time for optimization 0.144 sec. ( 0 min.)
#equal comm only
circE.MYM.ec=CircE.BFGS(data.merged.mym.cor,
v.names = rownames(data.merged.mym.cor),
m=2,N=n.participsnts.MY.M,r=1,equal.com = TRUE)Date: Wed Jul 5 14:09:42 2023
Data: Circumplex Estimation
Model:Equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 0.4866015 0.20280219 Inf -Inf
pleasant 4.4649237 -0.11096692 Inf -Inf
calm 4.3918000 -0.07656113 Inf -Inf
uneventful 2.9755937 0.20770125 Inf -Inf
monotonous 2.1126030 -0.04905213 Inf -Inf
annoying 1.3420145 -0.27243621 Inf -Inf
chaotic 0.9691427 -0.05621773 Inf -Inf
a 0 0.0380179 0.77358497 Inf 0
a 2 0.0000000 7.62870384 Inf 0
v 0.3975896 4.20977414 Inf 0
z eventful 0.8043718 0.58043893 Inf 0
z vibrant 0.8152370 0.59219998 Inf 0
z pleasant 0.9492864 -1.09771441 Inf 0
z calm 0.9437553 -1.01349993 Inf 0
z uneventful 0.7314296 2.13823789 Inf 0
z monotonous 0.6498327 5.22814923 Inf 0
z annoying 0.8489066 0.03729099 Inf 0
z chaotic 0.8798776 -0.38869130 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 1.492766
iter 2 value 1.271359
iter 3 value 0.908731
iter 4 value 0.821915
iter 5 value 0.744798
iter 6 value 0.723871
iter 7 value 0.709480
iter 8 value 0.703614
iter 9 value 0.693925
iter 10 value 0.671272
iter 11 value 0.654959
iter 12 value 0.647022
iter 13 value 0.645579
iter 14 value 0.644638
iter 15 value 0.641375
iter 16 value 0.638991
iter 17 value 0.637948
iter 18 value 0.637166
iter 19 value 0.636706
iter 20 value 0.636331
iter 21 value 0.636055
iter 22 value 0.635858
iter 23 value 0.635756
iter 24 value 0.635694
iter 25 value 0.635663
iter 26 value 0.635649
iter 27 value 0.635636
iter 28 value 0.635627
iter 29 value 0.635624
iter 30 value 0.635620
iter 31 value 0.635619
iter 32 value 0.635618
iter 33 value 0.635618
iter 34 value 0.635618
final value 0.635618
converged
Final gradient value:
[1] 1.534689e-05 2.138419e-04 2.308101e-04 1.601734e-04 -1.817346e-04
[6] -2.586752e-04 -1.191076e-04 -3.501802e-04 2.724371e-04 4.906707e-05
[11] 3.687079e-04 8.342898e-05 -1.120918e-04 -2.042152e-04 4.562137e-05
[16] -1.586126e-04 -5.096375e-04 -3.773902e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.636
-----------Population discrepancy function value, Fo
Point estimate : 0.035
Confidence Interval 90 % : ( 0 ; 0.526 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.044
Confidence Interval 90 % : ( 0 ; 0.171 )
-----------Discrepancy function TEST
TEST STATISTIC : 19.07
p values:
Ho: perfect fit (RMSEA=0.00) : 0.388
Ho: close fit (RMSEA=0.050) : 0.473
-----------Power estimation (alpha=0.05),
N 31
Degrees of freedom= 18
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.071
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.062
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.097
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.081
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.349
Confidence Interval 90 % : ( 2.314 ; 2.841 )
Hoelter's CN( .05 ) : 46
-----------Fit index
Chisquare (null model) = 158.6338 Df = 28
Bentler-Bonnett NFI : 0.88
Tucker-Lewis NNFI : 0.987
Bentler CFI : 0.992
SRMR : 0.114
GFI : 0.991
AGFI : 0.982
-----------Parsimony index
Akaike Information Criterion : -0.564
Bozdogans's Consistent AIC : -60.743
Schwarz's Bayesian Criterion : -1.425
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 23.21256 10.16500
pleasant 262.10115 14.04520
calm 257.41004 14.01999
uneventful 173.85489 11.32521
monotonous 139.57430 12.00082
annoying 85.16814 13.32132
chaotic 55.95285 12.32920
a 0 0.04484 0.02660
a 2 0.03934 0.03172
v 0.35371 0.08716
z eventful 0.95210 0.12736
z vibrant 0.89965 0.12269
z pleasant 0.74082 0.09886
z calm 0.74277 0.09935
z uneventful 1.00024 0.13366
z monotonous 0.99405 0.13380
z annoying 0.80329 0.10876
z chaotic 0.80547 0.11042
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind. (L
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.86 ( 0.8
vibrant 23 ( 3 ; 43 ) 337 ( 357 ; 317 ) 0.86 ( 0.8
chaotic 56 ( 32 ; 80 ) 304 ( 328 ; 280 ) 0.86 ( 0.8
annoying 85 ( 59 ; 111 ) 275 ( 301 ; 249 ) 0.86 ( 0.8
monotonous 140 ( 116 ; 163 ) 220 ( 244 ; 197 ) 0.86 ( 0.8
uneventful 174 ( 152 ; 196 ) 186 ( 208 ; 164 ) 0.86 ( 0.8
calm 257 ( 230 ; 285 ) 103 ( 130 ; 75 ) 0.86 ( 0.8
pleasant 262 ( 235 ; 290 ) 98 ( 125 ; 70 ) 0.86 ( 0.8
; U)
eventful ; 0.91 )
vibrant ; 0.91 )
chaotic ; 0.91 )
annoying ; 0.91 )
monotonous ; 0.91 )
uneventful ; 0.91 )
calm ; 0.91 )
pleasant ; 0.91 )
(MCSC) Correlation at 180 degrees: -0.845
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0414 0.9224 0.0363
----------------------------------------------------
CPU Time for optimization 0.098 sec. ( 0 min.)
circE.MYO.ec=CircE.BFGS(data.merged.myo.cor,
v.names = rownames(data.merged.myo.cor),
m=2,N=n.participsnts.MY.O,r=1,equal.com = TRUE)Date: Wed Jul 5 14:09:42 2023
Data: Circumplex Estimation
Model:Equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 0.45384745 -0.2147537787 Inf -Inf
pleasant 4.79117045 -0.0291642255 Inf -Inf
calm 4.68607733 -0.0268916664 Inf -Inf
uneventful 3.06659201 0.0004925923 Inf -Inf
monotonous 2.25528126 0.1226147457 Inf -Inf
annoying 1.65637071 0.0894978332 Inf -Inf
chaotic 1.31797429 0.1320971906 Inf -Inf
a 0 0.06620263 0.3106715291 Inf 0
a 2 0.00000000 3.8284408613 Inf 0
v 0.48519553 5.7641657834 Inf 0
z eventful 0.84830360 -0.2761854854 Inf 0
z vibrant 0.55540045 9.8262470291 Inf 0
z pleasant 0.93277567 -1.0803204593 Inf 0
z calm 0.92706956 -1.0138115488 Inf 0
z uneventful 0.84258910 -0.1689556965 Inf 0
z monotonous 0.65966327 3.7909603745 Inf 0
z annoying 0.82359756 -0.0102841035 Inf 0
z chaotic 0.81023595 0.2257295800 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 1.578753
iter 2 value 1.114552
iter 3 value 1.041658
iter 4 value 0.998737
iter 5 value 0.952856
iter 6 value 0.941536
iter 7 value 0.923158
iter 8 value 0.912088
iter 9 value 0.907848
iter 10 value 0.907517
iter 11 value 0.907004
iter 12 value 0.906739
iter 13 value 0.906638
iter 14 value 0.906434
iter 15 value 0.906351
iter 16 value 0.906293
iter 17 value 0.906275
iter 18 value 0.906268
iter 19 value 0.906264
iter 20 value 0.906261
iter 21 value 0.906257
iter 22 value 0.906256
iter 23 value 0.906255
iter 24 value 0.906255
iter 25 value 0.906254
iter 26 value 0.906254
iter 27 value 0.906254
final value 0.906254
converged
Final gradient value:
[1] -4.812285e-05 -2.271370e-05 2.512346e-05 1.129702e-04 5.565536e-05
[6] -1.473325e-04 7.063412e-05 -1.034968e-04 5.366695e-05 -9.962934e-05
[11] -1.012494e-04 -6.963419e-05 4.779564e-05 2.002980e-05 -1.089469e-04
[16] -9.325197e-05 2.763787e-05 1.654290e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.906
-----------Population discrepancy function value, Fo
Point estimate : 0.323
Confidence Interval 90 % : ( 0 ; 0.919 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.134
Confidence Interval 90 % : ( 0 ; 0.226 )
-----------Discrepancy function TEST
TEST STATISTIC : 28.09
p values:
Ho: perfect fit (RMSEA=0.00) : 0.061
Ho: close fit (RMSEA=0.050) : 0.098
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 18
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.071
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.062
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.099
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.082
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.54
Confidence Interval 90 % : ( 2.217 ; 3.136 )
Hoelter's CN( .05 ) : 33
-----------Fit index
Chisquare (null model) = 137.1038 Df = 28
Bentler-Bonnett NFI : 0.795
Tucker-Lewis NNFI : 0.856
Bentler CFI : 0.907
SRMR : 0.108
GFI : 0.925
AGFI : 0.85
-----------Parsimony index
Akaike Information Criterion : -0.255
Bozdogans's Consistent AIC : -52.289
Schwarz's Bayesian Criterion : -1.106
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 14.36766 11.74454
pleasant 273.56013 15.43450
calm 265.55193 15.48600
uneventful 173.82221 12.62581
monotonous 140.62375 13.53883
annoying 94.39136 15.14789
chaotic 68.51352 14.54744
a 0 0.05660 0.03573
a 2 0.00241 0.02776
v 0.54296 0.13481
z eventful 0.88201 0.12027
z vibrant 0.94475 0.12959
z pleasant 0.70635 0.09671
z calm 0.70832 0.09681
z uneventful 0.87639 0.11935
z monotonous 0.85946 0.11975
z annoying 0.75902 0.10482
z chaotic 0.77776 0.10787
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.81
vibrant 14 ( 351 ; 37 ) 346 ( 9 ; 323 ) 0.81
chaotic 69 ( 40 ; 97 ) 291 ( 320 ; 263 ) 0.81
annoying 94 ( 65 ; 124 ) 266 ( 295 ; 236 ) 0.81
monotonous 141 ( 114 ; 167 ) 219 ( 246 ; 193 ) 0.81
uneventful 174 ( 149 ; 199 ) 186 ( 211 ; 161 ) 0.81
calm 266 ( 235 ; 296 ) 94 ( 125 ; 64 ) 0.81
pleasant 274 ( 243 ; 304 ) 86 ( 117 ; 56 ) 0.81
(L ; U)
eventful ( 0.73 ; 0.87 )
vibrant ( 0.73 ; 0.87 )
chaotic ( 0.73 ; 0.87 )
annoying ( 0.73 ; 0.87 )
monotonous ( 0.73 ; 0.87 )
uneventful ( 0.73 ; 0.87 )
calm ( 0.73 ; 0.87 )
pleasant ( 0.73 ; 0.87 )
(MCSC) Correlation at 180 degrees: -0.889
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0534 0.9443 0.0023
----------------------------------------------------
CPU Time for optimization 0.079 sec. ( 0 min.)
circE.SG.ec=CircE.BFGS(data.merged.sg.cor,
v.names = rownames(data.merged.sg.cor),
m=2,N=n.participsnts.SG,r=1,equal.com = TRUE)Date: Wed Jul 5 14:09:42 2023
Data: Circumplex Estimation
Model:Equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 0.55278045 0.14218007 Inf -Inf
pleasant 5.00053540 -0.13801638 Inf -Inf
calm 4.91712634 -0.14443186 Inf -Inf
uneventful 3.10703919 0.05259313 Inf -Inf
monotonous 2.68269456 0.19077669 Inf -Inf
annoying 1.77776315 -0.10313097 Inf -Inf
chaotic 1.38051075 -0.02694885 Inf -Inf
a 0 0.02019079 4.52639626 Inf 0
a 2 0.00000000 5.09235075 Inf 0
v 0.43312236 7.65452351 Inf 0
z eventful 0.86361738 -0.19319545 Inf 0
z vibrant 0.70536260 2.96821399 Inf 0
z pleasant 0.94834443 -1.09010138 Inf 0
z calm 0.95084737 -1.11160304 Inf 0
z uneventful 0.83582880 0.33284606 Inf 0
z monotonous 0.52936793 14.24574293 Inf 0
z annoying 0.89007942 -0.68547798 Inf 0
z chaotic 0.86217789 -0.17137922 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 2.257207
iter 2 value 1.513869
iter 3 value 1.401812
iter 4 value 1.318278
iter 5 value 1.286532
iter 6 value 1.262219
iter 7 value 1.214699
iter 8 value 1.181337
iter 9 value 1.138454
iter 10 value 1.130101
iter 11 value 1.127665
iter 12 value 1.125578
iter 13 value 1.123258
iter 14 value 1.119916
iter 15 value 1.118630
iter 16 value 1.117521
iter 17 value 1.117022
iter 18 value 1.116526
iter 19 value 1.115852
iter 20 value 1.115615
iter 21 value 1.115503
iter 22 value 1.115480
iter 23 value 1.115462
iter 24 value 1.115454
iter 25 value 1.115437
iter 26 value 1.115416
iter 27 value 1.115392
iter 28 value 1.115372
iter 29 value 1.115365
iter 30 value 1.115359
iter 31 value 1.115358
iter 32 value 1.115357
iter 33 value 1.115357
final value 1.115357
converged
Final gradient value:
[1] 2.735709e-05 -1.855983e-04 -4.279453e-04 1.487570e-04 4.074807e-04
[6] 9.226475e-05 2.421311e-04 -4.992265e-04 -3.540888e-04 8.592016e-05
[11] -6.022755e-05 -1.643077e-04 -4.512488e-04 -2.824748e-04 8.952181e-05
[16] 9.705732e-05 3.698695e-04 3.159595e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 1.115
-----------Population discrepancy function value, Fo
Point estimate : 0.533
Confidence Interval 90 % : ( 0.118 ; 1.198 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.172
Confidence Interval 90 % : ( 0.081 ; 0.258 )
-----------Discrepancy function TEST
TEST STATISTIC : 34.58
p values:
Ho: perfect fit (RMSEA=0.00) : 0.011
Ho: close fit (RMSEA=0.050) : 0.021
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 18
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.071
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.062
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.099
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.082
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.75
Confidence Interval 90 % : ( 2.335 ; 3.415 )
Hoelter's CN( .05 ) : 27
-----------Fit index
Chisquare (null model) = 167.7914 Df = 28
Bentler-Bonnett NFI : 0.794
Tucker-Lewis NNFI : 0.816
Bentler CFI : 0.881
SRMR : 0.142
GFI : 0.882
AGFI : 0.764
-----------Parsimony index
Akaike Information Criterion : -0.046
Bozdogans's Consistent AIC : -45.807
Schwarz's Bayesian Criterion : -0.897
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 20.11733 10.02570
pleasant 284.41546 13.48214
calm 277.46578 13.66104
uneventful 178.16011 10.46988
monotonous 168.70395 10.55644
annoying 101.35477 13.44724
chaotic 66.01211 12.68769
a 0 0.03002 0.02244
a 2 0.00496 0.02011
v 0.39991 0.09631
z eventful 0.98147 0.12940
z vibrant 1.00202 0.13299
z pleasant 0.72220 0.09492
z calm 0.71712 0.09382
z uneventful 1.02137 0.13412
z monotonous 1.05391 0.13935
z annoying 0.74440 0.09820
z chaotic 0.80285 0.10763
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.85
vibrant 20 ( 0 ; 40 ) 340 ( 360 ; 320 ) 0.85
chaotic 66 ( 41 ; 91 ) 294 ( 319 ; 269 ) 0.85
annoying 101 ( 75 ; 128 ) 259 ( 285 ; 232 ) 0.85
monotonous 169 ( 148 ; 189 ) 191 ( 212 ; 171 ) 0.85
uneventful 178 ( 158 ; 199 ) 182 ( 202 ; 161 ) 0.85
calm 277 ( 251 ; 304 ) 83 ( 109 ; 56 ) 0.85
pleasant 284 ( 258 ; 311 ) 76 ( 102 ; 49 ) 0.85
(L ; U)
eventful ( 0.78 ; 0.89 )
vibrant ( 0.78 ; 0.89 )
chaotic ( 0.78 ; 0.89 )
annoying ( 0.78 ; 0.89 )
monotonous ( 0.78 ; 0.89 )
uneventful ( 0.78 ; 0.89 )
calm ( 0.78 ; 0.89 )
pleasant ( 0.78 ; 0.89 )
(MCSC) Correlation at 180 degrees: -0.932
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.029 0.9662 0.0048
----------------------------------------------------
CPU Time for optimization 0.092 sec. ( 0 min.)
circE.ARAUS.ec=CircE.BFGS(data.araus.cor,
v.names = rownames(data.araus.cor),
m=2,N=29,r=1,equal.com = TRUE)Date: Wed Jul 5 14:09:42 2023
Data: Circumplex Estimation
Model:Equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
vibrant 5.79194185 -0.03778402 Inf -Inf
pleasant 4.48269171 -0.02565814 Inf -Inf
calm 4.28559422 -0.06563248 Inf -Inf
uneventful 3.06502168 0.09989432 Inf -Inf
monotonous 2.82071606 -0.03365129 Inf -Inf
annoying 1.29014691 -0.05590764 Inf -Inf
chaotic 0.75010314 0.02461958 Inf -Inf
a 0 0.02324581 2.03978744 Inf 0
a 2 0.00000000 7.27236709 Inf 0
v 0.42022930 4.12855388 Inf 0
z eventful 0.85852186 -0.17985038 Inf 0
z vibrant 0.75390659 1.59416426 Inf 0
z pleasant 0.84833960 -0.10918673 Inf 0
z calm 0.87605968 -0.46047276 Inf 0
z uneventful 0.86139274 -0.26303844 Inf 0
z monotonous 0.72262722 2.60552903 Inf 0
z annoying 0.81972945 0.32387486 Inf 0
z chaotic 0.77090119 1.48600660 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 0.651049
iter 2 value 0.591471
iter 3 value 0.452531
iter 4 value 0.391408
iter 5 value 0.378908
iter 6 value 0.377298
iter 7 value 0.371811
iter 8 value 0.370499
iter 9 value 0.368686
iter 10 value 0.367247
iter 11 value 0.366313
iter 12 value 0.366067
iter 13 value 0.365772
iter 14 value 0.365698
iter 15 value 0.365628
iter 16 value 0.365587
iter 17 value 0.365552
iter 18 value 0.365547
iter 19 value 0.365544
iter 20 value 0.365540
iter 21 value 0.365535
iter 22 value 0.365533
iter 23 value 0.365532
iter 24 value 0.365531
iter 25 value 0.365531
final value 0.365531
converged
Final gradient value:
[1] -4.785170e-04 8.146098e-05 -1.813690e-04 -5.310683e-04 6.217731e-05
[6] -4.016674e-04 1.399656e-04 3.701446e-04 -6.823869e-04 -2.189871e-04
[11] 3.437098e-05 -4.017966e-04 -4.327329e-04 -2.829208e-04 -2.996181e-04
[16] -4.728641e-04 -4.323903e-04 -3.664561e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
-----------Sample discrepancy function value : 0.366
-----------Population discrepancy function value, Fo
Point estimate : 0
Confidence Interval 90 % : ( 0 ; 0.056 )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0
Confidence Interval 90 % : ( 0 ; 0.056 )
-----------Discrepancy function TEST
TEST STATISTIC : 10.23
p values:
Ho: perfect fit (RMSEA=0.00) : 0.924
Ho: close fit (RMSEA=0.050) : 0.946
-----------Power estimation (alpha=0.05),
N 29
Degrees of freedom= 18
Effective number of parameters= 18
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.069
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.061
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.094
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.079
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.538
Confidence Interval 90 % : ( 2.538 ; 2.594 )
Hoelter's CN( .05 ) : 79
-----------Fit index
Chisquare (null model) = 120.3928 Df = 28
Bentler-Bonnett NFI : 0.915
Tucker-Lewis NNFI : 1.131
Bentler CFI : 1
SRMR : 0.056
GFI : 1
AGFI : 1
-----------Parsimony index
Akaike Information Criterion : -0.92
Bozdogans's Consistent AIC : -68.376
Schwarz's Bayesian Criterion : -1.799
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 333.83646 12.20386
pleasant 256.91241 15.28679
calm 244.19154 15.04829
uneventful 176.01425 12.42072
monotonous 163.25003 12.58379
annoying 73.53133 15.15901
chaotic 38.65851 13.41384
a 0 0.01711 0.02388
a 2 0.01236 0.02805
v 0.51005 0.13613
z eventful 0.82957 0.12191
z vibrant 0.84265 0.12138
z pleasant 0.77734 0.11139
z calm 0.77416 0.11201
z uneventful 0.82870 0.12089
z monotonous 0.86924 0.12526
z annoying 0.78812 0.11309
z chaotic 0.83921 0.12521
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.81
chaotic 39 ( 12 ; 65 ) 321 ( 348 ; 295 ) 0.81
annoying 74 ( 44 ; 103 ) 286 ( 316 ; 257 ) 0.81
monotonous 163 ( 139 ; 188 ) 197 ( 221 ; 172 ) 0.81
uneventful 176 ( 152 ; 200 ) 184 ( 208 ; 160 ) 0.81
calm 244 ( 215 ; 274 ) 116 ( 145 ; 86 ) 0.81
pleasant 257 ( 227 ; 287 ) 103 ( 133 ; 73 ) 0.81
vibrant 334 ( 310 ; 358 ) 26 ( 50 ; 2 ) 0.81
(L ; U)
eventful ( 0.73 ; 0.88 )
chaotic ( 0.73 ; 0.88 )
annoying ( 0.73 ; 0.88 )
monotonous ( 0.73 ; 0.88 )
uneventful ( 0.73 ; 0.88 )
calm ( 0.73 ; 0.88 )
pleasant ( 0.73 ; 0.88 )
vibrant ( 0.73 ; 0.88 )
(MCSC) Correlation at 180 degrees: -0.943
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0166 0.9714 0.012
----------------------------------------------------
CPU Time for optimization 0.071 sec. ( 0 min.)
#equal comm and angles
circE.MYM.eca=CircE.BFGS(data.merged.mym.cor,
v.names = rownames(data.merged.mym.cor),
m=2,N=n.participsnts.MY.M,r=1,
equal.com = TRUE, equal.ang = TRUE)Date: Wed Jul 5 14:09:43 2023
Data: Circumplex Estimation
Model:Constrained model: equal spacing and equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.0380179 6.47757774 Inf 0
a 2 0.0000000 19.91704320 Inf 0
v 0.3975896 12.91521018 Inf 0
z eventful 0.8043718 0.98182454 Inf 0
z vibrant 0.8152370 3.73982800 Inf 0
z pleasant 0.9492864 0.00076239 Inf 0
z calm 0.9437553 -0.28855010 Inf 0
z uneventful 0.7314296 3.35432549 Inf 0
z monotonous 0.6498327 7.04880697 Inf 0
z annoying 0.8489066 0.28466980 Inf 0
z chaotic 0.8798776 -0.72451629 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 4.839705
iter 2 value 4.542925
iter 3 value 3.908782
iter 4 value 3.820228
iter 5 value 3.726939
iter 6 value 3.516048
iter 7 value 3.233424
iter 8 value 2.954967
iter 9 value 2.928560
iter 10 value 2.850696
iter 11 value 2.827487
iter 12 value 2.825768
iter 13 value 2.823537
iter 14 value 2.823156
iter 15 value 2.822877
iter 16 value 2.822571
iter 17 value 2.822156
iter 18 value 2.822098
iter 19 value 2.822007
iter 20 value 2.822002
iter 21 value 2.822001
final value 2.822001
converged
Final gradient value:
[1] -1.033276e-03 -4.278267e-01 -1.675007e-04 -1.481130e-04 -2.774895e-05
[6] -3.980268e-04 -2.507781e-04 2.060698e-05 -2.782864e-05 -4.760241e-06
[11] 4.830054e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: ONE PARAMETER ( a 2 ) IS ON A BOUNDARY.
-----------Model degrees of freedom= 25
Active Bound= 1
The appropriate distribution for the test statistic lies between
chi-squared distribution with 25 and with 25 + 1 degrees of freedom.
-----------Values enclosed in square brackets are based on 25 + 1 = 26 degrees of freedom.
-----------Sample discrepancy function value : 2.822
-----------Population discrepancy function value, Fo
Point estimate : 1.988 [ 1.952 ]
Confidence Interval 90 % : ( 1.177 [ 1.147 ] ; 3.045 [ 3.006 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.282 [ 0.274 ]
Confidence Interval 90 % : ( 0.217 [ 0.21 ] ; 0.349 [ 0.34 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 84.66
p values:
Ho: perfect fit (RMSEA=0.00) : 0 [ 0 ]
Ho: close fit (RMSEA=0.050) : 0 [ 0 ]
-----------Power estimation (alpha=0.05),
N 31
Degrees of freedom= 25 [ 26 ]
Effective number of parameters= 11
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.076 [ 0.077 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.064 [ 0.064 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.107 [ 0.108 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.087 [ 0.088 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 3.869 [ 3.866 ]
Confidence Interval 90 % : ( 3.058 [ 3.061 ] ; 4.926 [ 4.92 ] )
Hoelter's CN( .05 ) : 14 [ 15 ]
-----------Fit index
Chisquare (null model) = 158.6338 Df = 28
Bentler-Bonnett NFI : 0.466
Tucker-Lewis NNFI : 0.488 [ 0.516 ]
Bentler CFI : 0.543 [ 0.543 ]
SRMR : 0.307
GFI : 0.668 [ 0.672 ]
AGFI : 0.522 [ 0.546 ]
-----------Parsimony index
Akaike Information Criterion : 2.089
Bozdogans's Consistent AIC : 35.886
Schwarz's Bayesian Criterion : 1.563
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.03295 0.03819
a 2 0.00000 0.04811
v 0.80345 0.24769
z eventful 0.75184 0.11420
z vibrant 0.96537 0.14663
z pleasant 0.70544 0.10715
z calm 0.68272 0.10370
z uneventful 0.79273 0.12041
z monotonous 0.82303 0.12501
z annoying 0.69639 0.10578
z chaotic 0.65923 0.10013
NOTE! ACTIVE BOUNDS FOR: a 2 ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.74
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.74
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.74
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.74
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.74
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.74
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.74
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.74
(L ; U)
eventful ( 0.64 ; 0.83 )
vibrant ( 0.64 ; 0.83 )
pleasant ( 0.64 ; 0.83 )
calm ( 0.64 ; 0.83 )
uneventful ( 0.64 ; 0.83 )
monotonous ( 0.64 ; 0.83 )
annoying ( 0.64 ; 0.83 )
chaotic ( 0.64 ; 0.83 )
(MCSC) Correlation at 180 degrees: -0.936
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0319 0.9681 0
----------------------------------------------------
CPU Time for optimization 0.04 sec. ( 0 min.)
circE.MYO.eca=CircE.BFGS(data.merged.myo.cor,
v.names = rownames(data.merged.myo.cor),
m=2,N=n.participsnts.MY.O,r=1,
equal.com = TRUE, equal.ang = TRUE)Date: Wed Jul 5 14:09:43 2023
Data: Circumplex Estimation
Model:Constrained model: equal spacing and equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.06620263 3.0126583 Inf 0
a 2 0.00000000 12.0150869 Inf 0
v 0.48519553 9.9336890 Inf 0
z eventful 0.84830360 0.3032792 Inf 0
z vibrant 0.55540045 12.0536465 Inf 0
z pleasant 0.93277567 -0.5893785 Inf 0
z calm 0.92706956 -0.1744871 Inf 0
z uneventful 0.84258910 0.6844968 Inf 0
z monotonous 0.65966327 4.2473017 Inf 0
z annoying 0.82359756 -0.1332314 Inf 0
z chaotic 0.81023595 0.2032359 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 4.129872
iter 2 value 3.734176
iter 3 value 2.986475
iter 4 value 2.833485
iter 5 value 2.567885
iter 6 value 2.406389
iter 7 value 2.259527
iter 8 value 2.201461
iter 9 value 2.184733
iter 10 value 2.181120
iter 11 value 2.180278
iter 12 value 2.179882
iter 13 value 2.179224
iter 14 value 2.178266
iter 15 value 2.177511
iter 16 value 2.177282
iter 17 value 2.177090
iter 18 value 2.177087
iter 19 value 2.177087
final value 2.177087
converged
Final gradient value:
[1] -1.521449e-04 -1.241919e+00 -5.060692e-06 1.152732e-05 -3.178090e-05
[6] 4.534782e-05 -8.255902e-05 1.212589e-05 -6.699832e-05 1.004015e-04
[11] 4.583678e-05
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: ONE PARAMETER ( a 2 ) IS ON A BOUNDARY.
-----------Model degrees of freedom= 25
Active Bound= 1
The appropriate distribution for the test statistic lies between
chi-squared distribution with 25 and with 25 + 1 degrees of freedom.
-----------Values enclosed in square brackets are based on 25 + 1 = 26 degrees of freedom.
-----------Sample discrepancy function value : 2.177
-----------Population discrepancy function value, Fo
Point estimate : 1.369 [ 1.34 ]
Confidence Interval 90 % : ( 0.706 [ 0.674 ] ; 2.28 [ 2.247 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.234 [ 0.227 ]
Confidence Interval 90 % : ( 0.168 [ 0.161 ] ; 0.302 [ 0.294 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 67.49
p values:
Ho: perfect fit (RMSEA=0.00) : 0 [ 0 ]
Ho: close fit (RMSEA=0.050) : 0 [ 0 ]
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 25 [ 26 ]
Effective number of parameters= 11
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.077 [ 0.078 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.064 [ 0.064 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.109 [ 0.111 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.089 [ 0.09 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 3.175 [ 3.178 ]
Confidence Interval 90 % : ( 2.512 [ 2.513 ] ; 4.087 [ 4.086 ] )
Hoelter's CN( .05 ) : 18 [ 19 ]
-----------Fit index
Chisquare (null model) = 137.1038 Df = 28
Bentler-Bonnett NFI : 0.508
Tucker-Lewis NNFI : 0.564 [ 0.59 ]
Bentler CFI : 0.611 [ 0.611 ]
SRMR : 0.237
GFI : 0.745 [ 0.749 ]
AGFI : 0.633 [ 0.652 ]
-----------Parsimony index
Akaike Information Criterion : 1.467
Bozdogans's Consistent AIC : 18.367
Schwarz's Bayesian Criterion : 0.947
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.04038 0.04319
a 2 0.00000 0.05339
v 0.89938 0.28127
z eventful 0.75853 0.11639
z vibrant 0.87011 0.13351
z pleasant 0.66719 0.10238
z calm 0.69206 0.10619
z uneventful 0.76992 0.11814
z monotonous 0.75557 0.11594
z annoying 0.67007 0.10282
z chaotic 0.68422 0.10499
NOTE! ACTIVE BOUNDS FOR: a 2 ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.73
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.73
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.73
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.73
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.73
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.73
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.73
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.73
(L ; U)
eventful ( 0.61 ; 0.82 )
vibrant ( 0.61 ; 0.82 )
pleasant ( 0.61 ; 0.82 )
calm ( 0.61 ; 0.82 )
uneventful ( 0.61 ; 0.82 )
monotonous ( 0.61 ; 0.82 )
annoying ( 0.61 ; 0.82 )
chaotic ( 0.61 ; 0.82 )
(MCSC) Correlation at 180 degrees: -0.922
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0388 0.9612 0
----------------------------------------------------
CPU Time for optimization 0.043 sec. ( 0 min.)
circE.SG.eca=CircE.BFGS(data.merged.sg.cor,
v.names = rownames(data.merged.sg.cor),
m=2,N=n.participsnts.SG,r=1,
equal.com = TRUE, equal.ang = TRUE)Date: Wed Jul 5 14:09:43 2023
Data: Circumplex Estimation
Model:Constrained model: equal spacing and equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.02019079 8.74210684 Inf 0
a 2 0.00000000 14.26854247 Inf 0
v 0.43312236 14.15376207 Inf 0
z eventful 0.86361738 0.58671432 Inf 0
z vibrant 0.70536260 5.02472538 Inf 0
z pleasant 0.94834443 -0.61228695 Inf 0
z calm 0.95084737 0.01004503 Inf 0
z uneventful 0.83582880 1.29933341 Inf 0
z monotonous 0.52936793 14.31751745 Inf 0
z annoying 0.89007942 -0.35504008 Inf 0
z chaotic 0.86217789 0.36270651 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 5.435375
iter 2 value 5.127681
iter 3 value 4.197944
iter 4 value 4.024816
iter 5 value 3.510134
iter 6 value 3.225267
iter 7 value 3.159678
iter 8 value 3.143066
iter 9 value 3.134551
iter 10 value 3.131966
iter 11 value 3.131281
iter 12 value 3.129796
iter 13 value 3.128077
iter 14 value 3.126971
iter 15 value 3.126462
iter 16 value 3.126255
iter 17 value 3.126251
iter 18 value 3.126251
final value 3.126251
converged
Final gradient value:
[1] 2.877091e-04 -2.285724e+00 7.336036e-05 3.170134e-04 5.121337e-04
[6] -5.987061e-04 2.230693e-06 3.121737e-04 -1.938469e-04 -1.310940e-04
[11] -4.892957e-04
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: ONE PARAMETER ( a 2 ) IS ON A BOUNDARY.
-----------Model degrees of freedom= 25
Active Bound= 1
The appropriate distribution for the test statistic lies between
chi-squared distribution with 25 and with 25 + 1 degrees of freedom.
-----------Values enclosed in square brackets are based on 25 + 1 = 26 degrees of freedom.
-----------Sample discrepancy function value : 3.126
-----------Population discrepancy function value, Fo
Point estimate : 2.326 [ 2.293 ]
Confidence Interval 90 % : ( 1.464 [ 1.436 ] ; 3.422 [ 3.388 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.305 [ 0.297 ]
Confidence Interval 90 % : ( 0.242 [ 0.235 ] ; 0.37 [ 0.361 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 96.91
p values:
Ho: perfect fit (RMSEA=0.00) : 0 [ 0 ]
Ho: close fit (RMSEA=0.050) : 0 [ 0 ]
-----------Power estimation (alpha=0.05),
N 32
Degrees of freedom= 25 [ 26 ]
Effective number of parameters= 11
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.077 [ 0.078 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.064 [ 0.064 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.109 [ 0.111 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.089 [ 0.09 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 4.132 [ 4.132 ]
Confidence Interval 90 % : ( 3.271 [ 3.275 ] ; 5.229 [ 5.227 ] )
Hoelter's CN( .05 ) : 13 [ 13 ]
-----------Fit index
Chisquare (null model) = 167.7914 Df = 28
Bentler-Bonnett NFI : 0.422
Tucker-Lewis NNFI : 0.424 [ 0.454 ]
Bentler CFI : 0.486 [ 0.486 ]
SRMR : 0.269
GFI : 0.632 [ 0.636 ]
AGFI : 0.47 [ 0.496 ]
-----------Parsimony index
Akaike Information Criterion : 2.417
Bozdogans's Consistent AIC : 47.791
Schwarz's Bayesian Criterion : 1.896
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.00284 0.03548
a 2 0.00000 0.05518
v 0.96448 0.31134
z eventful 0.75188 0.11818
z vibrant 0.83301 0.13093
z pleasant 0.64904 0.10202
z calm 0.68026 0.10692
z uneventful 0.76670 0.12051
z monotonous 0.75296 0.11835
z annoying 0.65442 0.10286
z chaotic 0.67878 0.10669
NOTE! ACTIVE BOUNDS FOR: a 2 ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind. (L
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.71 ( 0.6
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.71 ( 0.6
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.71 ( 0.6
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.71 ( 0.6
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.71 ( 0.6
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.71 ( 0.6
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.71 ( 0.6
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.71 ( 0.6
; U)
eventful ; 0.81 )
vibrant ; 0.81 )
pleasant ; 0.81 )
calm ; 0.81 )
uneventful ; 0.81 )
monotonous ; 0.81 )
annoying ; 0.81 )
chaotic ; 0.81 )
(MCSC) Correlation at 180 degrees: -0.994
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0028 0.9972 0
----------------------------------------------------
CPU Time for optimization 0.035 sec. ( 0 min.)
circE.ARAUS.eca=CircE.BFGS(data.araus.cor,
v.names = rownames(data.araus.cor),
m=2,N=29,r=1,
equal.com = TRUE, equal.ang = TRUE)Date: Wed Jul 5 14:09:43 2023
Data: Circumplex Estimation
Model:Constrained model: equal spacing and equal radius
Reference variable at 0 degree: eventful
-------------------------------
Initial parameters:
-------------------------------
parameter initial gradient upper lower
a 0 0.02324581 2.5342743 Inf 0
a 2 0.00000000 5.3912856 Inf 0
v 0.42022930 6.5822046 Inf 0
z eventful 0.85852186 -0.3983879 Inf 0
z vibrant 0.75390659 2.0654519 Inf 0
z pleasant 0.84833960 0.4120611 Inf 0
z calm 0.87605968 -0.2688604 Inf 0
z uneventful 0.86139274 -0.2472112 Inf 0
z monotonous 0.72262722 3.4936352 Inf 0
z annoying 0.81972945 1.0714546 Inf 0
z chaotic 0.77090119 1.2792824 Inf 0
Constrained (L-BFGS-B) Optimization
Warning in optim(start, func, gr = gradient, control = control, method =
"L-BFGS-B", : method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and
'abstol'
iter 1 value 1.663087
iter 2 value 1.137575
iter 3 value 1.093136
iter 4 value 1.060498
iter 5 value 1.036472
iter 6 value 1.022176
iter 7 value 1.019157
iter 8 value 1.016335
iter 9 value 1.015118
iter 10 value 1.013639
iter 11 value 1.011605
iter 12 value 1.009519
iter 13 value 1.009014
iter 14 value 1.008563
iter 15 value 1.008548
iter 16 value 1.008547
iter 17 value 1.008547
final value 1.008547
converged
Final gradient value:
[1] 1.135584e-05 -1.832449e+00 6.213118e-05 1.299697e-04 1.589948e-05
[6] -1.980959e-05 -3.713392e-05 3.151902e-05 1.137010e-04 -1.363710e-04
[11] -2.244845e-05
Warning in if (class(solve.hess) == "try-error") {: the condition has length > 1
and only the first element will be used
=================================
MEASURES OF FIT OF THE MODEL
=================================
NOTE: ONE PARAMETER ( a 2 ) IS ON A BOUNDARY.
-----------Model degrees of freedom= 25
Active Bound= 1
The appropriate distribution for the test statistic lies between
chi-squared distribution with 25 and with 25 + 1 degrees of freedom.
-----------Values enclosed in square brackets are based on 25 + 1 = 26 degrees of freedom.
-----------Sample discrepancy function value : 1.009
-----------Population discrepancy function value, Fo
Point estimate : 0.116 [ 0.079 ]
Confidence Interval 90 % : ( 0 [ 0 ] ; 0.731 [ 0.691 ] )
-----------ROOT MEAN SQUARE ERROR OF APPROXIMATION
Steiger-Lind: RMSEA=sqrt(Fo/Df)
Point estimate : 0.068 [ 0.055 ]
Confidence Interval 90 % : ( 0 [ 0 ] ; 0.171 [ 0.163 ] )
-----------Discrepancy function TEST
TEST STATISTIC : 28.24
p values:
Ho: perfect fit (RMSEA=0.00) : 0.297 [ 0.347 ]
Ho: close fit (RMSEA=0.050) : 0.387 [ 0.442 ]
-----------Power estimation (alpha=0.05),
N 29
Degrees of freedom= 25 [ 26 ]
Effective number of parameters= 11
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.01) : 0.074 [ 0.075 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.06) : 0.063 [ 0.063 ]
Ho (RMSEA=0.05) vs Alternative (RMSEA=0.08) : 0.103 [ 0.104 ]
Ho (RMSEA=0.00) vs Alternative (RMSEA=0.05) : 0.084 [ 0.085 ]
-----------EXPECTED CROSS VALIDATION INDEX
Browne and Cudeck's Index BCI-(MODIFIED AIC)
Point estimate : 2.166 [ 2.165 ]
Confidence Interval 90 % : ( 2.051 [ 2.086 ] ; 2.782 [ 2.777 ] )
Hoelter's CN( .05 ) : 38 [ 39 ]
-----------Fit index
Chisquare (null model) = 120.3928 Df = 28
Bentler-Bonnett NFI : 0.765
Tucker-Lewis NNFI : 0.961 [ 0.974 ]
Bentler CFI : 0.965 [ 0.965 ]
SRMR : 0.168
GFI : 0.972 [ 0.981 ]
AGFI : 0.96 [ 0.974 ]
-----------Parsimony index
Akaike Information Criterion : 0.223
Bozdogans's Consistent AIC : -19.801
Schwarz's Bayesian Criterion : -0.314
----------------------------------------
Parameter estimates and Standard Errors
----------------------------------------
Parameters Stand. Errors
eventful 0.00000 0.00000
vibrant 45.00000 0.00000
pleasant 90.00000 0.00000
calm 135.00000 0.00000
uneventful 180.00000 0.00000
monotonous 225.00000 0.00000
annoying 270.00000 0.00000
chaotic 315.00000 0.00000
a 0 0.01242 0.02845
a 2 0.00000 0.04046
v 0.66678 0.20409
z eventful 0.73567 0.11118
z vibrant 0.80781 0.12208
z pleasant 0.78297 0.11832
z calm 0.74739 0.11295
z uneventful 0.74414 0.11246
z monotonous 0.84160 0.12718
z annoying 0.79649 0.12037
z chaotic 0.75841 0.11461
NOTE! ACTIVE BOUNDS FOR: a 2 ;
---------------------------------------------------------------------------
Estimates (ML) of POLAR ANGLES and COMMUNALITY INDICES
(approximate, 95 % one at time confidence intervals)
Note: variable names have been reordered to yield increasing polar angles
---------------------------------------------------------------------------
ang. pos. (L ; U) 360-ang. pos. (L ; U) comm. ind.
eventful 0 ( 0 ; 0 ) 360 ( 360 ; 360 ) 0.77
vibrant 45 ( 45 ; 45 ) 315 ( 315 ; 315 ) 0.77
pleasant 90 ( 90 ; 90 ) 270 ( 270 ; 270 ) 0.77
calm 135 ( 135 ; 135 ) 225 ( 225 ; 225 ) 0.77
uneventful 180 ( 180 ; 180 ) 180 ( 180 ; 180 ) 0.77
monotonous 225 ( 225 ; 225 ) 135 ( 135 ; 135 ) 0.77
annoying 270 ( 270 ; 270 ) 90 ( 90 ; 90 ) 0.77
chaotic 315 ( 315 ; 315 ) 45 ( 45 ; 45 ) 0.77
(L ; U)
eventful ( 0.67 ; 0.86 )
vibrant ( 0.67 ; 0.86 )
pleasant ( 0.67 ; 0.86 )
calm ( 0.67 ; 0.86 )
uneventful ( 0.67 ; 0.86 )
monotonous ( 0.67 ; 0.86 )
annoying ( 0.67 ; 0.86 )
chaotic ( 0.67 ; 0.86 )
(MCSC) Correlation at 180 degrees: -0.975
----------------------------------------------------
b 0 b 1 b 2
Estimates of Betas: 0.0123 0.9877 0
----------------------------------------------------
CPU Time for optimization 0.034 sec. ( 0 min.)
#table of model fitting parameters
ssm.circE.all<-rbind(
cbind(circE.MYM.q$CFI, circE.MYO.q$CFI,
circE.SG.q$CFI, circE.ARAUS.q$CFI),
cbind(circE.MYM.q$RMSEA, circE.MYO.q$RMSEA,
circE.SG.q$RMSEA, circE.ARAUS.q$RMSEA),
cbind(circE.MYM.q$SRMR, circE.MYO.q$SRMR,
circE.SG.q$SRMR, circE.ARAUS.q$SRMR),
cbind(circE.MYM.ea$CFI, circE.MYO.ea$CFI,
circE.SG.ea$CFI, circE.ARAUS.ea$CFI),
cbind(circE.MYM.ea$RMSEA, circE.MYO.ea$RMSEA,
circE.SG.ea$RMSEA, circE.ARAUS.ea$RMSEA),
cbind(circE.MYM.ea$SRMR, circE.MYO.ea$SRMR,
circE.SG.ea$SRMR, circE.ARAUS.ea$SRMR),
cbind(circE.MYM.ec$CFI, circE.MYO.ec$CFI,
circE.SG.ec$CFI, circE.ARAUS.ec$CFI),
cbind(circE.MYM.ec$RMSEA, circE.MYO.ec$RMSEA,
circE.SG.ec$RMSEA, circE.ARAUS.ec$RMSEA),
cbind(circE.MYM.ec$SRMR, circE.MYO.ec$SRMR,
circE.SG.ec$SRMR, circE.ARAUS.ec$SRMR),
cbind(circE.MYM.eca$CFI, circE.MYO.eca$CFI,
circE.SG.eca$CFI, circE.ARAUS.eca$CFI),
cbind(circE.MYM.eca$RMSEA, circE.MYO.eca$RMSEA,
circE.SG.eca$RMSEA, circE.ARAUS.eca$RMSEA),
cbind(circE.MYM.eca$SRMR, circE.MYO.eca$SRMR,
circE.SG.eca$SRMR, circE.ARAUS.eca$SRMR),
c(round(res.ssm.mean$results$fit_est,3)[-1],
round(res.ssm.mean$results$fit_est,3)[1])) %>%
as.data.frame(.) %>%
dplyr::mutate_all(function(x) format(x, nsmall=3)) %>% #format to 3 dec pl
`colnames<-`(c("MY:M","MY:O","SG","ARAUS")) %>%
`rownames<-`(c("CFIq","RMSEAq","SRMRq",
"CFIea","RMSEAea","SRMRea",
"CFIec","RMSEAec","SRMRec",
"CFIeca","RMSEAeca","SRMReca",
"SSM"))
models.table.new <- ci.rthorr %>%
dplyr::mutate(CIp=paste0(round(CI,3)," (",round(p,3),")")) %>%
dplyr::select(CIp,ETHNICITY) %>%
pivot_wider(names_from = ETHNICITY,values_from = CIp) %>%
as.data.frame(.) %>%
`rownames<-`(c("CI (p)")) %>%
rbind(.,ssm.circE.all) %>%
cbind(c("","\\ge0.90","\\ge0.13","< 0.06",
"\\ge0.90","\\ge0.13","< 0.06",
"\\ge0.90","\\ge0.13","< 0.06",
"\\ge0.90","\\ge0.13","< 0.06","> 0.7"),.) %>%
`colnames<-`(c("","MY:M","MY:O","SG","ARAUS")) %>%
kableExtra::kbl(booktabs = T, linesep = "",
#format = "latex",
format = "html",
label = "modelfitnew",
caption = "Summary of model fitting indexes") %>%
#kable_styling(latex_table_env = "tabularx") %>%
kable_styling(protect_latex = TRUE) %>%
kable_paper(full_width = T) #%>%
#save_kable(paste0(getwd(),"/Table tex files/modelfitnew2.tex"))
models.table.new| MY:M | MY:O | SG | ARAUS | ||
|---|---|---|---|---|---|
| CI (p) | 0.597 (0.002) | 0.681 (0.002) | 0.701 (0.002) | 0.847 (0) | |
| CFIq | \ge0.90 | 1.000 | 1.000 | 1.000 | 1.000 |
| RMSEAq | \ge0.13 | 0.000 | 0.000 | 0.000 | 0.000 |
| SRMRq | < 0.06 | 0.016 | 0.020 | 0.037 | 0.023 |
| CFIea | \ge0.90 | 0.734 | 0.825 | 0.656 | 0.949 |
| RMSEAea | \ge0.13 | 0.254 | 0.185 | 0.293 | 0.097 |
| SRMRea | < 0.06 | 0.254 | 0.217 | 0.231 | 0.159 |
| CFIec | \ge0.90 | 0.992 | 0.907 | 0.881 | 1.000 |
| RMSEAec | \ge0.13 | 0.044 | 0.134 | 0.172 | 0.000 |
| SRMRec | < 0.06 | 0.114 | 0.108 | 0.142 | 0.056 |
| CFIeca | \ge0.90 | 0.543 | 0.611 | 0.486 | 0.965 |
| RMSEAeca | \ge0.13 | 0.282 | 0.234 | 0.305 | 0.068 |
| SRMReca | < 0.06 | 0.307 | 0.237 | 0.269 | 0.168 |
| SSM | > 0.7 | 0.395 | 0.637 | 0.706 | 0.803 |
Summary of model fitting indexes