ptest: Permutation test for group differences at within-subject...

In ClusterBootstrap: Analyze Clustered Data with Generalized Linear Models using the Cluster Bootstrap

Description

Perform permutation tests for differences between two groups at given within-subject levels in a long-formatted dataframe

Usage

ptest(
  data,
  outcome,
  within,
  between,
  at.within,
  at.between,
  pn = 1000,
  progress.bar = TRUE
)

Arguments

data	dataframe that contains the data in long format.
outcome	outcome variable (i.e., the variable for which the difference should be tested).
within	within-subject variable.
between	between-subjects variable.
at.within	determine for which within-subject levels (e.g., which timepoint) the difference should be tested.
at.between	determine the groups in the difference test (should always be of length 2).
pn	the number of permutations that should be performed.
progress.bar	indicates whether a progress bar will be shown.

Details

In every permutation cycle, the outcome variable gets permutated and the Welch t test statistic is calculated.

Value

ptest produces an object of class "clusbootptest", containing the following relevant components:

perm.statistics	A matrix of length(at.within) rows and pn columns, containing the Welch t-test statics for all permutations within the at.within level in the columns. The first column contains the t statistic for the observed data.
pvalues	Data frame containing the p values for every at.within level.

Author(s)

Mathijs Deen, Mark de Rooij

See Also

A useful method for the obtained clusbootptest class object is plot.clusbootptest.

Examples

## Not run: 
medication <- medication[medication$time %% 1 == 0,]
set.seed(1)
permtest.1 <- ptest(data = meds, outcome = pos, within = time, between = treat, 
                    at.within = c(0,2,4,6), at.between = c(0,1), pn = 2000)
permtest.1$pvalues
## End(Not run)

ClusterBootstrap documentation built on Feb. 16, 2021, 5:06 p.m.