permute_pvalue: Calculation of permutation p-value
In suzhesuzhe/GEM: Generated Effect Modifier

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 `c("nu","de","F")`, which corresponde to the numerator, denominator and F-statistics criteria respectively. The default method is the F-statistics method.

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

#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")