nparcomp-package {nparcomp} | R Documentation |
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.
Package: | nparcomp |
Type: | Package |
Version: | 1.0-0 |
Date: | 2008-05-04 |
License: | GPL |
Frank Konietschke
Maintainer: Frank Konietschke <fkoniet@gwdg.de>
Konietschke, F., Brunner, E., Hothorn, L.A. (2008). Nonparametric Relative Contrast Effects: Asymptotic Theory and Small Sample Approximations, Konietschke, F., Brunner, E., Hothorn, L.A. (2008). Simultaneous Confidence Intervals for Relative Effects in Dunnett Comparsisons, 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.
# two sample comparisons: Nonparametric Behrens-Fisher Problem data(impla) npar.t.test(impla~group, data = impla, asy.method = "t.app", p.perm = TRUE, alternative = "two.sided") npar.t.test(impla~group, data = impla, asy.method = "logit", p.perm = TRUE, alternative = "two.sided") # no permutation test npar.t.test(impla~group, data = impla, asy.method = "t.app", p.perm = FALSE, alternative = "two.sided") #--Analysis of relative contrast effects in different contrast settings data(liver) # Williams Contrast nparcomp(weight ~dosage, data=liver, asy.method = "probit", type = "Williams", alternative = "two.sided", plot.simci = TRUE, info = TRUE) # Dunnett Contrast nparcomp(weight ~dosage, data=liver, asy.method = "probit", type = "Dunnett", alternative = "two.sided", plot.simci = TRUE, info = TRUE) # Dunnett dose 3 is baseline nparcomp(weight ~dosage, data=liver, asy.method = "probit", type = "Dunnett", control = "3",alternative = "two.sided", plot.simci = TRUE, info = TRUE) data(colu) # Tukey comparison - one sided(lower) nparcomp(corpora ~dose, data=colu, asy.method = "mult.t", type = "Tukey",alternative = "lower", plot.simci = TRUE, info = TRUE) # Tukey comparison- one sided(greater) nparcomp(corpora ~dose, data=colu, asy.method = "mult.t", type = "Tukey",alternative = "greater", plot.simci = TRUE, info = TRUE) # Tukey comparison- one sided(lower) nparcomp(corpora ~dose, data=colu, asy.method = "mult.t", type = "Tukey",alternative = "lower", plot.simci = TRUE, info = TRUE) # Marcus comparison- one sided(greater) nparcomp(corpora ~dose, data=colu, asy.method = "logit", type = "Marcus",alternative = "greater", plot.simci = TRUE, info = TRUE)