multcomp (version 0.4-8)

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=1e+06, nlevel=NULL, 
       nzerocol=c(0,0),...)
## S3 method for class 'formula':
simint(formula, data=list(), subset, na.action, whichf, ...)
## S3 method for class 'lm':
simint(y, psubset=NULL, conf.level=0.95, cmatrix = NULL, 
       alternative=c("two.sided","less", "greater"), 
       asympt=FALSE, eps=0.001, maxpts=1e+06, ...)

Arguments

y

a numeric vector of responses or an object of class lm or glm.

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".

nzerocol

a vector of two elements defining the number of zero-columns to add to the contrast matrix from left (the first element, usually 1 for the intercept) and right (usually 0 if no covariables are specified). nzerocol is automatically determined

psubset

a vector of integers or characters indicating for which subset of coefficients of a (generalized) linear model y simultaneous confidences intervals should be computed.

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").

whichf

if more than one factor is given in the right hand side of formula, whichf can be used to defined the factor to compute contrasts of.

...

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.

See simtest for detailed information on the formula interface.

References

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

See Also

TukeyHSD for the special case of Tukey contrasts.

Examples

data(recovery)

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

# alternatively via a prespecified linear model
lmmod <- lm(minutes ~ blanket, data=recovery, contrasts=list(blanket =
            "contr.Dunnett"))
summary(simint(lmmod, psubset=2:4, conf.level=0.9,
               alternative="less",eps=0.0001))

# Tukey confidence intervals, compare with TukeyHSD

data(warpbreaks)
fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)
tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE)
print(tHSD)

mcHSD <- simint(breaks ~ wool + tension, data = warpbreaks, 
                whichf="tension", type="Tukey")
print(mcHSD)
plot(mcHSD)

<testonly>tlow <- tHSD$tension[,2]
  tupp <- tHSD$tension[,3]
  mclow <- mcHSD$conf.int[,1]
  mcupp <- mcHSD$conf.int[,2]

  a <- round(tlow, 1) 
  b <- round(mclow, 1)
  names(a) <- NULL
  names(b) <- NULL
  stopifnot(all.equal(a, b))

  a <- round(tupp, 1) 
  b <- round(mcupp, 1)
  names(a) <- NULL
  names(b) <- NULL
  stopifnot(all.equal(a, b))</testonly>