mutoss (version 0.1-12)

nparcomp: Simultaneous confidence intervals for relative contrast effects...

Description

Simultaneous confidence intervals for relative contrast effects The procedure controls the FWER in the strong sense.

Usage

nparcomp(formula, data, type=c("UserDefined", "Tukey", "Dunnett",
    "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus",
    "UmbrellaWilliams"), control=NULL, conflevel=0.95,
    alternative=c("two.sided", "less", "greater"), rounds=3,
    correlation=FALSE, asy.method=c("logit", "probit", "normal",
    "mult.t"), plot.simci=FALSE, info=TRUE, contrastMatrix=NULL)

Value

A list containing:

adjPValues

A numeric vector containing the adjusted pValues

rejected

A logical vector indicating which hypotheses are rejected

confIntervals

A matrix containing the estimates and the lower and upper confidence bound

errorControl

A Mutoss S4 class of type errorControl, containing the type of error controlled by the function.

Author

FrankKonietschke

Arguments

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

The confidence level for the 1 - conflevel confidence intervals. By default it is 0.05

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

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

contrastMatrix

arbitrary contrast matrix given by the user

Details

With this function, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes adjusted 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. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers

Examples

if (FALSE) # TODO Check this example and set a seed!
grp <- rep(1:5,10)
x <- rnorm(50, grp)
dataframe <- data.frame(x,grp)
# Williams Contrast
nparcomp(x ~grp, data=dataframe, asy.method = "probit",
type = "Williams", alternative = "two.sided", plot.simci = TRUE, info = TRUE)

# Dunnett Contrast
nparcomp(x ~grp, data=dataframe, asy.method = "probit",control=1,
type = "Dunnett", alternative = "two.sided", plot.simci = TRUE, info = TRUE)

# Dunnett dose 3 is baseline
nparcomp(x ~grp, data=dataframe, asy.method = "probit",
type = "Dunnett", control = "3",alternative = "two.sided",
plot.simci = TRUE, info = TRUE)