mctp.rm: Nonparametric multiple contrast tests and simultaneous...

In nparcomp: Multiple Comparisons and Simultaneous Confidence Intervals

Description

The function mctp.rm computes the estimator of nonparametric relative effects based on global rankings, simultaneous confidence intervals for the effects, and adjusted p-values based on contrasts in the setting of a repeated measures design with n independent individuals and d repeated measures. Contrasts include "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams", "GrandMean", and "UserDefined". The statistics are computed using multivariate normal distribution, multivariate Satterthwaite t-Approximation, and multivariate transformations (adjusted log odds or Fisher function). The function 'mctp.rm' computes both the one-sided and two-sided simultaneous confidence intervals and adjusted p-values. The confidence intervals can be plotted.

Usage

mctp.rm(formula, data, type = c("Tukey", "Dunnett", "Sequen", 
     "Williams", "Changepoint", "AVE", "McDermott", "Marcus", 
     "UmbrellaWilliams", "GrandMean", "UserDefined"), 
     conf.level = 0.95, alternative = c("two.sided", "less", 
     "greater"), asy.method = c("log.odds", "fisher", "mult.t", 
     "normal"), plot.simci = FALSE, control = NULL, info = TRUE, 
     rounds = 3, contrast.matrix = NULL, correlation = FALSE, 
     const=1/1.702)        

Arguments

formula	A two-sided 'formula' specifying a numeric response variable and a repeated measures factor with more than two levels. If the factor contains less than 3 levels, an error message will be returned.
data	A dataframe containing the variables specified in formula.
type	Character string defining the type of contrast. It should be one of "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams", "GrandMean", "UserDefined".
conf.level	The confidence level for conf.level-confidence intervals (default is 0.95).
alternative	Character string defining the alternative hypothesis, one of "two.sided", "less", or "greater".
asy.method	Character string defining the asymptotic approximation method, one of "log.odds" (for using the adjusted log odds effect sizes), "mult.t" (for using a multivariate t-distribution with a Satterthwaite Approximation), "fisher" (for using the Fisher transformation function), or "normal" (for using the multivariate normal distribution).
plot.simci	A logical indicating whether you want a plot of the confidence intervals.
control	Character string defining the control group in Dunnett comparisons. By default, it is the first group by definition of the factor variable.
info	A logical whether you want a brief overview with informations about the output.
rounds	Number of rounds for the numeric values of the output (default is 3).
contrast.matrix	User-defined contrast matrix.
correlation	A logical whether the estimated correlation matrix and covariance matrix should be printed.
const	Number used for the adjustment of log odds when the "log.odds" option is chosen.

Value

Data.Info	List of samples and sample sizes and estimated effect per repeated measures level.
Contrast	Contrast matrix.
Analysis	Estimator: Estimated relative effect, Lower: Lower limit of the simultaneous confidence intervals, Upper: Upper limit of the simultaneous confidence intervals, Statistic: Test statistic p.Value: Adjusted p-values for the hypothesis by the choosen approximation method.
Analysis.Inf	The same as Analysis except that it assumes rounds = Inf.
Overall	The critical value and adjusted p-value for the overall hypothesis.
input	List of input arguments by user.
text.Output	Character string specifying the alternative hypotheses.
connames	Character string specifying the contrast names.
AsyMethod	Character string specifying the approximation method.

Note

Estimated relative effects with 0 or 1 are replaced with 0.001 and 0.999.

A summary and a graph can be created separately by using the functions summary.mctp.rm and plot.mctp.rm.

For the analysis, the R packages 'multcomp' and 'mvtnorm' are required.

Author(s)

Marius Placzek, Kimihiro Noguchi

References

F. Konietschke, A.C. Bathke, L.A. Hothorn, E. Brunner: Testing and estimation of purely nonparametric effects in repeated measures designs. Computational Statistics and Data Analysis 54 (2010) 1895-1905.

See Also

To analyse simple one-way layouts with independent samples use mctp.

Examples

## Not run: 
data(panic)
a<-mctp.rm(CGI~week, data=panic, type = "Dunnett",
           alternative = "two.sided",
           asy.method = "log.odds", plot.simci = FALSE,
           info = FALSE, contrast.matrix = NULL)
summary(a)
plot(a)

b<-mctp.rm(CGI~week, data=panic, type = "Dunnett",
           alternative = "two.sided",
           asy.method = "mult.t", plot.simci = FALSE,
           info = FALSE, contrast.matrix = NULL)
summary(b)
plot(b)

c<-mctp.rm(CGI~week, data=panic, type = "Dunnett",
           alternative = "two.sided",
           asy.method = "fisher", plot.simci = FALSE,
           info = FALSE, contrast.matrix = NULL)
summary(c)
plot(c)

d<-mctp.rm(CGI~week, data=panic, type = "Tukey",
        alternative = "two.sided",
        asy.method = "mult.t", plot.simci = TRUE)
summary(d)

## End(Not run)

nparcomp documentation built on June 25, 2019, 5:02 p.m.