mctp: Nonparametric multiple contrast tests and simultaneous...
In nparcomp: Multiple Comparisons and Simultaneous Confidence Intervals

Description Usage Arguments Value Note Author(s) References See Also Examples

The function mctp 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 independent samples. 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' computes both the one-sided and two-sided simultaneous confidence intervals and adjusted p-values. The simultaneous confidence intervals can be plotted.

mctp(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("fisher", "mult.t", 
     "normal", "log.odds"), plot.simci = FALSE, control = NULL, info = TRUE, 
     rounds = 3, contrast.matrix = NULL, correlation = FALSE, 
     effect=c("unweighted","weighted"), const=1/1.702)

`formula`	A two-sided 'formula' specifying a numeric response variable and a 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 "fisher" (for using the Fisher transformation function), "log.odds" (for using the adjusted log odds effect sizes), "mult.t" (for using a multivariate t-distribution with a Satterthwaite Approximation), , or "normal" (for using the multivariate normal distribution), "log.odds" (for using the adjusted log odds effect sizes).
`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.
`effect`	Character string defining the type of effect, one of "unweighted" and "weighted".
`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 group.
`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: 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.
`text.output.W`	Character string specifying the weight pattern for the reference distribution.
`connames`	Character string specifying the contrast names.
`AsyMethod`	Character string specifying the approximation method.

If the samples are completely seperated the variance estimators are Zero by construction. In these cases the Null-estimators are replaced by 0.001. Estimated relative effects with 0 or 1 are replaced with 0.001, 0.999 respectively.

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

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

Frank Konietschke, Kimihiro Noguchi

F. Konietschke, L.A. Hothorn, E. Brunner: Rank-Based Multiple Test Procedures and Simultaneous Confidence Intervals. Electronic Journal of Statistics, Vol.0 (2011) 1-8.

Konietschke, F., Placzek, M., Schaarschmidt, S., Hothorn, L.A. (2015). nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals. Journal of Statistical Software, 61(10), 1-17.

For simultaneous confidence intervals for relative contrast effects, see nparcomp.

## Not run: 
data(liver)

  # Williams Contrast

a<-mctp(weight ~dosage, data=liver, asy.method = "fisher",
        type = "Williams", alternative = "two.sided", 
        plot.simci = TRUE, info = FALSE)
summary(a)

 # Dunnett Contrast

b<-mctp(weight ~dosage, data=liver, asy.method = "fisher",
        type = "Dunnett", alternative = "two.sided", 
        plot.simci = TRUE, info = FALSE)
summary(b)

 # Dunnett dose 3 is baseline

c<-mctp(weight ~dosage, data=liver, asy.method = "log.odds",
        type = "Dunnett", control = "3",alternative = "two.sided",
        plot.simci = TRUE, info = FALSE)
summary(c)


data(colu)

  # Tukey comparison- one sided (less)

a<-mctp(corpora~ dose, data=colu, asy.method = "log.odds",
        type = "Tukey",alternative = "less", 
        plot.simci = TRUE, info = FALSE)
summary(a)

 # Tukey comparison- one sided (greater)

b<-mctp(corpora~ dose, data=colu, asy.method = "mult.t",
        type = "Tukey",alternative = "greater", 
        plot.simci = TRUE, info = FALSE)
summary(b)

  # Tukey comparison- one sided (less)

c<-mctp(corpora~ dose, data=colu, asy.method = "mult.t",
        type = "Tukey",alternative = "less", 
        plot.simci = TRUE, info = FALSE)
summary(c)

 # Marcus comparison- one sided (greater)

d<-mctp(corpora~ dose, data=colu, asy.method = "fisher",
        type = "Marcus",alternative = "greater", 
        plot.simci = TRUE, info = FALSE)
summary(d)

## End(Not run)