## Adjusted p-values for the number of true hypotheses.

### Description

Calculates adjusted p-values for the number of true hypotheses on the basis of the closed testing procedure.

### Usage

 adjusted (closure, reject, n=0) 

### Arguments

 closure An object of class closure, typically created through a call to closed . reject A character vector identifying the hypotheses to be rejected. Must be a subvector of hypotheses(closure). n The maximum number of false null hypotheses allowed.

### Details

The function pick calculates adjusted p-values for intersection hypotheses of interest.

### Value

The function returns a p-value (numeric).

### 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$"Pr(" # for R >= 2.14.0 if (is.null(res)) res <- anov$"P("   # 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?