The function nparcomp computes the estimator of nonparametric relative contrast effects, simultaneous confidence intervals for
the effects and simultaneous p-values based on special contrasts like "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint",
"AVE", "McDermott", "Marcus", "UmbrellaWilliams", "UserDefined". The statistics are computed using multivariate normal distribution, multivariate Satterthwaite t-Approximation
and multivariate transformations (Probit and Logit transformation function).
The function 'nparcomp' also computes one-sided and two-sided confidence intervals and p-values. The confidence intervals can be plotted.
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", "UserDefined".
control
Character string defining the control group in Dunnett comparisons. By default it is the first group by
definition of the dataset.
conf.level
The confidence level for the conflevel confidence intervals (default is 0.95).
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. By default it is rounds=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 "logit", for using the logit transformation function, "probit", for using the probit transformation function, "normal",
for using the multivariate normal distribution or "mult.t" for
using a multivariate t-distribution with a Satterthwaite Approximation.
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.
weight.matrix
A logical indicating whether the weight matrix should be printed.
Value
Data.Info
List of samples and sample sizes.
Contrast
Contrast matrix.
Analysis
Comparison: relative contrast effect ,
relative.effect: estimated relative contrast effect,
Estimator: Estimated relative contrast 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.
Note
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.nparcomp and plot.nparcomp.
For the analysis, the R packages 'multcomp' and 'mvtnorm' are required.
Author(s)
Frank Konietschke
References
Konietschke, F., Brunner, E., Hothorn, L.A. (2008). Nonparametric Relative Contrast Effects: Asymptotic Theory and Small Sample Approximations.
Munzel. U., Hothorn, L.A. (2001). A unified Approach to Simultaneous Rank Tests Procedures in the Unbalanced One-way Layout. Biometric Journal, 43, 553-569.
See Also
For two-sample comparisons based on relative effects, see npar.t.test.
Examples
data(liver)
# Williams Contrast
a<-nparcomp(weight ~dosage, data=liver, asy.method = "probit",
type = "Williams", alternative = "two.sided",
plot.simci = TRUE, info = FALSE,correlation=TRUE)
summary(a)
# Dunnett dose 3 is baseline
c<-nparcomp(weight ~dosage, data=liver, asy.method = "probit",
type = "Dunnett", control = "3",
alternative = "two.sided", info = FALSE)
summary(c)
plot(c)
data(colu)
# Tukey comparison- one sided(lower)
a<-nparcomp(corpora~ dose, data=colu, asy.method = "mult.t",
type = "Tukey",alternative = "less",
plot.simci = TRUE, info = FALSE)
summary(a)
# Tukey comparison- one sided(greater)
b<-nparcomp(corpora~ dose, data=colu, asy.method = "mult.t",
type = "Tukey",alternative = "greater",
plot.simci = TRUE, info = FALSE)
summary(b)