nparcomp: Simultaneous confidence intervals for relative contrast...

In mutoss: Unified Multiple Testing Procedures

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)

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

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(s)

FrankKonietschke

Examples

## Not run: # 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)

## End(Not run)

mutoss documentation built on March 31, 2023, 8:46 p.m.