simplesimint {BSagri}R Documentation

Simultaneous confidence intervals from raw estimates

Description

Calculates simultaneous confidence intervals for multiple contrasts based on a parameter vector, its variance-covariance matrix and (optionally) the degrees of freedom, using quantiles of the multivar

Usage

simplesimint(coef, vcov, cmat, df = NULL, conf.level = 0.95,
 alternative = c("two.sided", "less", "greater"))

Arguments

coef a single numeric vector, specifying the point estimates of the parameters of interest
vcov the variance-covariance matrix corresponding to coef, should be of dimension P-times-P, when coef is of P
cmat the contrasts matrix specifying the comparisons of interest with respect to coef, should have P columns, when coef is of length p
df optional, the degree of freedom for the multivariate t-distribution; if specified, quantiles from the multivariate t-distribution are used for confidence interval estimation, if not specified (default), quantiles of the multivariate normal distribution are used
conf.level a single numeric value between 0.5 and 1.0; the simultaneous confidence level
alternative a single character string, "two.sided" for intervals, "less" for upper limits, and "greater" for lower limits

Details

Implements the methods formerly available in package multcomp, function csimint. Input values are a vector of parameter estimates mu of length P, a corresponding estimate for its variance-covariance matrix Sigma (P times P), and a contrast matrix C of dimension M times P. The contrasts L = C * mu are computed, the variance-covariance matrix (being a function of C and Sigma) and the corresponding correlation matrix R are computed. Finally, confidence intervals for L are computed: if df is given, quantiles of an M-dimensional t distribution with correlation matrix R are used, otherwise quantiles of an M-dimensional standard normal distribution with correlation matrix R are used.

Value

An object of class "simplesimint"

estimate the estimates of the contrasts
lower the lower confidence limits
upper the upper confidence limits
cmat the contrast matrix, as input
alternative a character string, as input
conf.level a numeric value, as input
quantile a numeric value, the quantile used for confidence interval estimation
df a numeric value or NULL, as input
stderr the standard error of the contrasts
vcovC the variance covariance matrix of the contrasts

Note

This is a testversion and has not been checked extensively. Please report bugs.

Author(s)

Frank Schaarschmidt

See Also

See ?coef and ?vcov for extracting of parameter vectors and corresponding variance covariance matrices from variou model fits.

Examples


# For the simple case of Gaussian response
# variables with homoscedastic variance,
# see the following example

library(mratios)
data(angina)

boxplot(response ~ dose, data=angina)

# Fit a cell means model,

fit<-lm(response ~ 0+dose, data=angina)

# extract cell means, the corresponding
# variance-covariance matrix and the
# residual degree of freedom,

cofi<-coef(fit)
vcofi<-vcov(fit)
dofi<-fit$df.residual

# define an appropriate contrast matrix,
# here, comparisons to control

n<-unlist(lapply(split(angina$response, f=angina$dose), length))
names(n)<-names(cofi)

cmat<-contrMat(n=n, type="Dunnett")
cmat

#

test<-simplesimint(coef=cofi, vcov=vcofi, df=dofi, cmat=cmat, alternative="greater" )

test

summary(test)

plotCI(test)

### Note, that the same result can be achieved much more conveniently
### using confint.glht in package multcomp


[Package BSagri version 0.1-5 Index]