Adjusted {cherry} | R Documentation |
Calculates adjusted p-values for the number of true hypotheses on the basis of the closed testing procedure.
adjusted (closure, reject, n=0)
closure |
An object of class |
reject |
A character vector identifying the hypotheses to be rejected. Must be a subvector of |
n |
The maximum number of false null hypotheses allowed. |
The function pick
calculates adjusted p-values for intersection hypotheses of interest.
The function returns a p-value (numeric).
Jelle Goeman: j.j.goeman@lumc.nl
# 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 with ajdusted p-values
cl <- closed(mytest, hypotheses, alpha=NA)
# What is the adjusted p-value of the intersection of the following hypotheses?
adjusted(cl, c("ht", "lwt", "smoke", "ui"))
# From what confidence level would we conclude
# that more than 2 of the following hypotheses would be false?
adjusted(cl, c("ht", "lwt", "smoke", "ui"), n=2)