nparcomp {nparcomp} | R Documentation |
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". 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 are plotted.
nparcomp(formula, data, type = c("Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus"), control = NULL, conflevel = 0.95, alternative = c("two.sided", "lower", "greater"), rounds = 3, Correlation = FALSE, asy.method = c("logit", "probit", "normal", "mult.t"), plot.simci = TRUE, info = TRUE)
formula |
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 |
data A dataframe containing the variables specified in formula |
type |
type Character string defining the type of contrast. It should be one of
"Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus" |
control |
control Character string defining the control group in Dunnett comparisons. By default it is the first group by
lexicographical ordering |
conflevel |
conflevel The confidence level for the 1 - conflevel confidence intervals. By default it is 0.05 |
alternative |
alternative Character string defining the alternative hypothesis, one
of "two.sided", "lower" or "greater" |
rounds |
Number of rounds for the numeric values of the output. By default it is rounds=3 |
Correlation |
Correlation A logical whether the estimated correlation matrix and covariance matrix should be printed |
asy.method |
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 |
plot.simci A logical indicating whether you want a plot of the confidence intervals |
info |
info A logical whether you want a brief overview with informations about the output |
weight.matrix |
The weight matrix for the choosen nonparametric relative contrast effect |
Data.Info |
List of samples and sample sizes |
relative.effects |
Comparison: relative contrast effect , relative.effect: estimated relative contrast effect, confidence.interval: simultaneous confidence interval for relative contrast effect, t.value: teststatistic p.value: simultaneous p-values for the hypothesis by the choosen approximation method |
If the samples are completely seperated the variance estimators are Zero by construction. In these cases the Null-estimator are replaced by 0.001. Estimated relative effects with 0 or 1 are replaced with 0.001, 0.999 respectively. For the analysis, the R packages 'multcomp' and 'mvtnorm' are required.
Frank Konietschke
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.
For two-sample comparisons based on relative effects, see npar.t.test
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)