simplesimint: Simultaneous confidence intervals from raw estimates
In BSagri: Safety Assessment in Agricultural Field Trials

Description Usage Arguments Details Value Note Author(s) See Also Examples

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

1 2	simplesimint(coef, vcov, cmat, df = NULL, conf.level = 0.95, alternative = c("two.sided", "less", "greater"))

`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

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.

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

This is a testversion and has not been checked extensively.

Frank Schaarschmidt

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

# 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