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Use gDelta.1 for sampling weights #1

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Use gDelta.1 for sampling weights #1

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6b7b28b
Use gDelta.1 for sampling weights
kecolson Apr 21, 2015
9e0df17
Correct typo
kecolson Apr 21, 2015
a8ec0ba
Add gDelta.1 documentation to roxygen
kecolson Apr 21, 2015
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15 changes: 13 additions & 2 deletions R/tmlecte.R
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,10 @@ print.cte <- function(x, ...) {
##' @param gDelta.SL.library SuperLearner library for estimating
##' \code{P(Delta=1|A,B)} if SuperLearner is used.
##' @param gDelta.1 User specified estimates of \code{P(Delta=1|A,B)}. Should be the same length as
##' \code{Y} if \code{gDelta.method} is \code{"user"}.
##' \code{Y} if \code{gDelta.method} is \code{"user"}. This parameter can also be used to specify
##' matching or sampling weights by setting gDelta.1 = 1/weights. If this option is specified,
##' the user must also set gDelta.method = "user" and Delta = 1 for all observations (See Rudolf
##' et al below).
##' @param family \code{gaussian} for a continuous outcome \code{Y},
##' \code{binomial} for a biniomial outcome
##' @param tol convergence criterion, set to something small
Expand All @@ -88,6 +91,9 @@ print.cte <- function(x, ...) {
##'perLearner calls
##' @return An object of class \code{cte}
##' @author Sam Lendle \email{lendle@@stat.berkeley.edu}
##' @references Rudolf KE, Diaz I, Rosenblum M, Stuart EA (2014) Estimating population treatment
##' effects from a survey subsample. American Journal of Epidemiology DOI: 10.1093/aje/kwu197.
##' \url{http://aje.oxfordjournals.org/content/early/2014/09/04/aje.kwu197.full.pdf+html}
##' @export
tmle.cte <- function(A, B, Y, a=0, Delta=NULL, Q.method="glm", Q.formula=NULL, Q.SL.library=NULL, Q.A1=NULL, Q.A0=NULL, g.method="glm", g.formula=NULL, g.SL.library=c("SL.glm", "SL.step", "SL.knn"), g.A1=NULL, gDelta.method="glm", gDelta.formula=NULL, gDelta.SL.library=c("SL.glm", "SL.step", "SL.knn"), gDelta.1=NULL, family=gaussian(), tol=1e-10, maxiter=100, target=TRUE, verbose=FALSE, Qbound=c(1e-10, 1-1e-10), gbound=c(1e-10, 1-1e-10), ...) {

Expand Down Expand Up @@ -181,7 +187,12 @@ tmle.cte <- function(A, B, Y, a=0, Delta=NULL, Q.method="glm", Q.formula=NULL, Q
}
gDelta.1 <- .bound(gDelta.1, c(1, min(gbound)))
} else {
gDelta.1 <- rep(1, length(Y))
if (gDelta.method=="user" && !is.null(gDelta.1) && sum(!is.na(gDelta.1))>0) {
# If there are no missing values for outcome, but gDelta.1 is specified by the user,
# Do nothing. Leave as is.
} else {
gDelta.1 <- rep(1, length(Y))
}
}

fail=FALSE
Expand Down