mctp.rm {nparcomp} | R Documentation |
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.
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)
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 |
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. |
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 |
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. |
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.
Marius Placzek, Kimihiro Noguchi
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.
To analyse simple one-way layouts with independent samples use mctp
.
## 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)