Nparcomp: Nonparametric relative contrast effects.

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Description

With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers.

Details

Package: nparcomp
Type: Package
Version: 1.0-0
Date: 2012-06-22
License: GPL

Author(s)

Frank Konietschke

Maintainer: Frank Konietschke <fkoniet@gwdg.de>

References

Konietschke, F. (2009). Simultane Konfidenzintervalle fuer nichtparametrische relative Kontrasteffekte. PhD-thesis, University of Goettingen.

Konietschke, F., Brunner, E., Hothorn, L.A. (2008). Nonparametric Relative Contrast Effects: Asymptotic Theory and Small Sample Approximations, Research report.

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.

Examples

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# two sample comparisons: Nonparametric Behrens-Fisher Problem

data(impla)
a<-npar.t.test(impla~group, data = impla,
               method = "t.app", 
               alternative = "two.sided")
summary(a)
plot(a)



#--Analysis of relative contrast effects in different contrast settings

data(liver)

 # Williams Contrast

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


 # Dunnett dose 3 is baseline

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



data(colu)

  # Tukey comparison - one sided(lower)

a<-nparcomp(corpora~ dose, data=colu, asy.method = "mult.t",
 type = "Tukey",alternative = "less")
summary(a)
plot(a)

  # Tukey comparison- one sided(greater)

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