npar.t.test.paired {nparcomp}

A 2-sample nonparametric studentized permutation test for paired data

Description

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

H_0: 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

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.

See Also

For multiple comparison procedures based on relative effects, see nparcomp.

Examples

## Not run: 

data(PGI)
a<-npar.t.test.paired(PGIscore~timepoint, data = PGI, 
               alternative = "two.sided", info=FALSE, plot.simci=FALSE)
summary(a)
plot(a)
               

## End(Not run)