# npar.t.test.paired: A 2-sample nonparametric studentized permutation test for... In nparcomp: Multiple Comparisons and Simultaneous Confidence Intervals

## Description

The function npar.t.test.paired performs a two sample studentized permutation test for paired data, that is testing the hypothesis

H0: p=1/2,

where p denotes the relative effect of 2 dependent samples, and computes a confidence interval for the relative effect p. In addition the Brunner-Munzel-Test accompanied by a confidence interval for the relative effect is implemented. npar.t.test.paired also computes one-sided and two-sided confidence intervals and p-values. The confidence interval can be plotted.

## Usage

 ```1 2 3``` ```npar.t.test.paired(formula, data, conf.level = 0.95, alternative = c("two.sided", "less", "greater"), nperm=10000, rounds = 3, info = TRUE, plot.simci = TRUE) ```

## Arguments

 `formula` A two-sided 'formula' specifying a numeric response variable and a factor with two levels. If the factor contains more than two levels, an error message will be returned. `data` A dataframe containing the variables specified in formula. `conf.level` The confidence level (default is 0.95). `alternative` Character string defining the alternative hypothesis, one of "two.sided", "less" or "greater". `nperm` The number of permutations for the studentized permutation test. By default it is nperm=10,000. `rounds` Number of rounds for the numeric values of the output (default is 3). `info` A logical whether you want a brief overview with informations about the output. `plot.simci` A logical indicating whether you want a plot of the confidence interval.

## Value

 ` Info ` List of samples and sample sizes. `Analysis ` Effect: relative effect p(a,b) of the two samples 'a' and 'b', p.hat: estimated relative effect, Lower: Lower limit of the confidence interval, Upper: Upper limit of the confidence interval, T: studentized teststatistic p.value: p-value for the hypothesis. ` input ` List of input by user.

## Note

A summary and a graph can be created separately by using the functions `summary.nparttestpaired` and `plot.nparttestpaired`.

Make sure that your dataset is ordered by subjects before applying npar.t.test.paired.

## Author(s)

Frank Konietschke

## References

Munzel, U., Brunner, E. (2002). An Exact Paired Rank Test. Biometrical Journal 44, 584-593.

Konietschke, F., Pauly, M. (2012). A Studentized Permutation Test for the Nonparametric Behrens-Fisher Problem in Paired Data. Electronic Journal of Statistic, Vol 6, 1358-1372.

For multiple comparison procedures based on relative effects, see `nparcomp`.
 ```1 2 3 4 5 6``` ```data(PGI) a<-npar.t.test.paired(PGIscore~timepoint, data = PGI, alternative = "two.sided", info=FALSE, plot.simci=FALSE) summary(a) plot(a) ```