| mctp.rm {nparcomp} | R Documentation |
In the setting of a repeated measures design with n independent individuals and d repeated measures 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 special contrasts like "UserDefined", "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams" are provided. The statistics are computed using multivariate normal distribution, multivariate Satterthwaite t-Approximation and multivariate transformations (Fisher function). The function 'mctp.rm' also computes one-sided and two-sided confidence intervals and p-values. The confidence intervals can be plotted.
mctp.rm(formula, data, type = c("UserDefined", "Tukey", "Dunnett",
"Sequen", "Williams", "Changepoint", "AVE", "McDermott",
"Marcus", "UmbrellaWilliams"), control = NULL, conf.level = 0.95,
alternative = c("two.sided", "lower", "greater"), rounds = 3,
correlation = FALSE,
asy.method = c("fisher", "normal", "mult.t"),
plot.simci = FALSE, info = TRUE, contrast.matrix = NULL)
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 "UserDefined", "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams". |
control |
If type=Dunnett, specification of the factor code which should serve as control (first level is default). |
conf.level |
The confidence level for |
alternative |
Character string defining the alternative hypothesis, one of "two.sided", "less" or "greater". |
rounds |
Number of rounds for the numeric values of the output (default is 3). |
correlation |
A logical whether the estimated correlation matrix and covariance matrix should be printed. |
asy.method |
Character string defining the asymptotic approximation method, one of "mult.t" for using a multivariate t-distribution with a Satterthwaite Approximation, "fisher" for using the Fisher transformation function, "normal", for using the multivariate normal distribution. |
plot.simci |
A logical indicating whether you want a plot of the confidence intervals. |
info |
A logical whether you want a brief overview with informations about the output. |
contrast.matrix |
User defined contrast matrix. |
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 interval, Upper: Upper limit of the simultaneous confidence interval, Statistic: Teststatistic p.Value: Adjusted p-values for the hypothesis by the choosen approximation method. |
input |
List of input by user. |
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
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.
data(panic)
a<-mctp.rm(CGI~week, data=panic, type = "Dunnett",
alternative = "two.sided",
asy.method = "mult.t", plot.simci = FALSE,
info = FALSE, contrast.matrix = NULL)
summary(a)
plot(a)
mctp.rm(CGI~week, data=panic, type = "Tukey",
alternative = "two.sided",
asy.method = "mult.t", plot.simci = TRUE)