Pick {cherry} | R Documentation |
Confidence limits for the number of true hypotheses.
Description
Calculates confidence limits for the number of true hypotheses on the basis of the closed testing procedure.
Usage
pick (closure, reject, alpha, silent=FALSE, plot=FALSE)
Arguments
closure |
An object of class |
reject |
A character vector identifying the hypotheses to be rejected. Must be a subvector of |
alpha |
For closure objects with adjusted p-values, specifies the value of alpha for which confidence limits are to be calculated (optional). |
silent |
If |
plot |
Whether a a confidence distribution should be plotted. Only available for closure objects with adjusted p-values. |
Details
The function pick
calculates a confidence interval for the number of true hypotheses among a selected set of hypotheses.
Value
The function returns the upper confidence limit for the number of true hypotheses among the set reject
. The lower confidence limit is always equal to 0. If closed
was called with alpha=NA
, a confidence distribution is plotted and returned.
Author(s)
Jelle Goeman: j.j.goeman@lumc.nl
Examples
# Example: the birthwt data set from the MASS library
# We want to find variables associated with low birth weight
library(MASS)
fullfit <- glm(low~age+lwt+race+smoke+ptl+ht+ui+ftv, family=binomial, data=birthwt)
hypotheses <- c("age", "lwt", "race", "smoke", "ptl", "ht", "ui", "ftv")
# Define the local test to be used in the closed testing procedure
mytest <- function(hyps) {
others <- setdiff(hypotheses, hyps)
form <- formula(paste(c("low~", paste(c("1", others), collapse="+"))))
anov <- anova(glm(form, data=birthwt, family=binomial), fullfit, test="Chisq")
res <- anov$"Pr("[2] # for R >= 2.14.0
if (is.null(res)) res <- anov$"P("[2] # earlier versions
res
}
# perform the closed testing
cl <- closed(mytest, hypotheses)
summary(cl)
# how many variables among a chosen set are associated with the response?
pick(cl, c("ht", "lwt", "smoke", "ui"))