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Last data update: 2014.03.03

R: Simultaneous confidence intervals from raw estimates

simplesimint

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.

Author(s)

Frank Schaarschmidt

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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(BSagri)
Loading required package: gamlss
Loading required package: splines
Loading required package: gamlss.data
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: nlme
Loading required package: parallel
 **********   GAMLSS Version 4.4-0  ********** 
For more on GAMLSS look at http://www.gamlss.org/
Type gamlssNews() to see new features/changes/bug fixes.

Loading required package: multcomp
Loading required package: mvtnorm
Loading required package: survival

Attaching package: 'survival'

The following object is masked from 'package:gamlss.data':

    leukemia

Loading required package: TH.data

Attaching package: 'TH.data'

The following object is masked from 'package:MASS':

    geyser

Loading required package: MCPAN
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BSagri/simplesimint.Rd_%03d_medium.png", width=480, height=480)
> ### Name: simplesimint
> ### Title: Simultaneous confidence intervals from raw estimates
> ### Aliases: simplesimint
> ### Keywords: htest
> 
> ### ** 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

	 Multiple Comparisons of Means: Dunnett Contrasts

              dose0 dose1 dose2 dose3 dose4
dose1 - dose0    -1     1     0     0     0
dose2 - dose0    -1     0     1     0     0
dose3 - dose0    -1     0     0     1     0
dose4 - dose0    -1     0     0     0     1
> 
> #
> 
> test<-simplesimint(coef=cofi, vcov=vcofi, df=dofi, cmat=cmat, alternative="greater" )
> 
> test

 Simultaneous 95 % confidence intervals: 
              Estimate       Lower Upper
dose1 - dose0    2.095 -1.34767238   Inf
dose2 - dose0    3.397 -0.04567238   Inf
dose3 - dose0    4.995  1.55232762   Inf
dose4 - dose0   10.499  7.05632762   Inf
> 
> summary(test)

 Simultaneous 95% confidence intervals: 
              Estimate       Lower Upper
dose1 - dose0    2.095 -1.34767238   Inf
dose2 - dose0    3.397 -0.04567238   Inf
dose3 - dose0    4.995  1.55232762   Inf
dose4 - dose0   10.499  7.05632762   Inf

 Used quantile: -2.2226,
obtained from a 4 -variate t-distribution
with 45 degrees of freedom.

 Used contrast matrix: 

	 Multiple Comparisons of Means: Dunnett Contrasts

              dose0 dose1 dose2 dose3 dose4
dose1 - dose0    -1     1     0     0     0
dose2 - dose0    -1     0     1     0     0
dose3 - dose0    -1     0     0     1     0
dose4 - dose0    -1     0     0     0     1

 Resulting variance covariance matrix of the contrasts: 
              dose1 - dose0 dose2 - dose0 dose3 - dose0 dose4 - dose0
dose1 - dose0      2.399287      1.199643      1.199643      1.199643
dose2 - dose0      1.199643      2.399287      1.199643      1.199643
dose3 - dose0      1.199643      1.199643      2.399287      1.199643
dose4 - dose0      1.199643      1.199643      1.199643      2.399287

 Corresponding correlation matrix of the contrasts: 
              dose1 - dose0 dose2 - dose0 dose3 - dose0 dose4 - dose0
dose1 - dose0           1.0           0.5           0.5           0.5
dose2 - dose0           0.5           1.0           0.5           0.5
dose3 - dose0           0.5           0.5           1.0           0.5
dose4 - dose0           0.5           0.5           0.5           1.0
> 
> plotCI(test)
> 
> ### Note, that the same result can be achieved much more conveniently
> ### using confint.glht in package multcomp
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>