multcomp (version 0.2-6)

simint: Simultaneous Confidence Intervals

Description

Computes simultaneous intervals for several multiple procedures.

Usage

## S3 method for class 'default':
simint(y, x=NULL, type=c("Dunnett", "Tukey",
       "Sequen", "AVE", "Changepoint", "Williams", "Marcus",
       "McDermott","Tetrade"), cmatrix=NULL, conf.level=0.95,
       alternative=c("two.sided","less", "greater"), asympt=FALSE,
       eps=0.001, maxpts=1000000, nlevel=NULL, ...)
## S3 method for class 'formula':
simint(formula, data=list(), subset, na.action, ...)

Arguments

y

a numeric vector of responses.

x

a numeric matrix, the design matrix.

type

the type of contrast to be used.

cmatrix

the contrast matrix itself can be specified. If cmatrix is defined, type is ignored.

conf.level

confidence level.

alternative

the alternative hypothesis must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

asympt

a logical indicating whether the (exact) t-distribution or the normal approximation should be used.

eps

absolute error tolerance as double.

maxpts

maximum number of function values as integer.

nlevel

a vector containing the number of levels for each factor for type == "Tetrade".

formula

a symbolic description of the model to be fit.

data

an optional data frame containing the variables in the model. By default the variables are taken from Environment(formula), typically the environment from which simint is called.

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when the data contain NA's. Defaults to GetOption("na.action").

...

further arguments to be passed to or from methods.

Value

• an object of class hmtest

Details

Computes simultaneous confidence intervals for several multiple comparisons. The implemented algorithms take the stochastical correlations between the test statistics into account. Only single step comparisons are performed. The present function allows for multiple comparisons of generally correlated means in general linear models under the classical ANOVA assumptions, as well as more general approximate procedures for approximately normal and generally correlated parameter estimates. Either multivariate normal or multivariate t statistics can be used. The interface allows the use of the multiple comparison procedures as for example Dunnett and Tukey. The resulting confidence intervals are not associated with the p-values from simtest.

References

Frank Bretz, Alan Genz and Ludwig A. Hothorn (2001), On the numerical availability of multiple comparison procedures. Biometrical Journal, 43(5), 645--656.

Examples

data(recover)

# one-sided simultaneous confidence intervals for Dunnett 
# in the one-way layout
summary(simint(minutes~blanket, data=recover, type="Dunnett", conf.level=0.9, 
       alternative="less",eps=0.0001))