Description Usage Arguments Value Examples
Calculates the permutation p-value for a fitted GEM. See more detail in E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics, (First published online: July 27, 2016). doi: 10.1093/biostatistics/kxw035.
1 | permute_pvalue(dat, permuteN, method = "F")
|
dat |
Data frame with first column as the treatment index, second column as the outcome, and the remaining columns as the covariates design matrix. The elements of the treatment index take K distinct values, where K is the number of treatment groups. The outcome has to be a continuous variable. |
permuteN |
Number of permutation |
method |
Choice of the criterion that the generated effect modifier optimizes. This is a string in
|
perm_p
Permutation p-value for the data and choosen criterior
p
A vector of calculated p-value for the original and permuted data set under the choosen criterior
1 2 3 4 5 6 7 8 9 | #constructing the covariance matrix
co <- matrix(0.2, 10, 10)
diag(co) <- 1
#simulate a data set
dataEx <- data_generator1(d = 0.3, R2 = 0.5, v2 = 1, n = 300,
co = co, beta1 = rep(1,10),inter = c(0,0))
#calculate the permuted p value
dat <- dataEx[[1]]
permute_pvalue(dat, permuteN = 200, method = "nu")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.