gmodels (version 2.0.7)

estimable: Contrasts and estimable linear functions of model coefficients for lm, glm, lme, and geese objects

Description

Compute and test contrasts and other estimable linear functions of model coefficients for for lm, glm, lme, and geese objects

Usage

estimable(obj, cm, beta0, conf.int=NULL, joint.test=FALSE, show.beta0)
.wald(obj, cm,beta0=rep(0, ifelse(is.null(nrow(cm)), 1, nrow(cm))))

Arguments

obj

Regression (lm,glm,lme) object.

cm

Matrix specifying estimable linear functions or contrasts (one per row). The number of columns must match the number of fitted coefficients in the model.

beta0

Vector of null hypothesis values

conf.int

Confidence level. If provided, confidence intervals will be computed.

joint.test

Logical value. If TRUE a 'joint' Wald test for the hypothesis $L \beta=\beta_0$ is performed. Otherwise 'row-wise' tests are performed, i.e. $(L \beta)_i=\beta_{0i}$

show.beta0

Logical value. If TRUE a column for beta0 will be included in the output table. Defaults to TRUE when beta0 is specified, FALSE otherwise.

Value

• Returns a matrix with one row per linear function. Columns contain the beta0 value (optional, see show.beta0 above), estimated coefficients, standard errors, t values, degrees of freedom, two-sided p-values, and the lower and upper endpoints of the 1-alpha confidence intervals.

Details

estimable computes an estimate, test statitic, significance test, and (optional) confidence interval for each linear functions of the model coefficients specified by the rows of cm. The estimates and their variances are obtained by applying the matrix cm to the model estimates variance-covariance matrix. Degrees of freedom are obtained from the appropriate model terms. The user is responsible for ensuring that the specified linear functions are meaningful.

For computing contrasts among levels of a single factor, fit.contrast may be more convenient. For computing contrasts between two specific combinations of model parameters, the contrast function in Frank Harrell's Design library may be more convenient.

The .wald function is called internally by estimable and is not intended for direct use.

See Also

fit.contrast, lm, lme, contrasts, contrast,

Examples

# simple contrast and confidence interval
y <- rnorm(100)
x <-  cut(rnorm(100, mean=y, sd=0.25),c(-4,-1.5,0,1.5,4))
reg <- lm(y ~ x)
estimable(reg, c(    0,   1,    0,   -1) )

# Fit a spline with a single knot at 0.5 and plot the *pointwise*
# confidence intervals
library(gplots)
x2 <- rnorm(100,mean=y,sd=0.5)
reg2 <- lm(y ~ x + x2 + pmax(x2-0.5,0) )
range <- seq(-2,2,,50)
tmp <- estimable(reg2,cbind(1,0,0,1,range,pmax(range-0.5,0)), conf.int=0.95)
plotCI(x=range,y=tmp[,1],li=tmp[,6],ui=tmp[,7])

# Fit both linear and quasi-Poisson models to iris data, then compute
# conficence intervals on a contrast.
data(iris)
lm1  <- lm(Sepal.Length~Sepal.Width+Species+Sepal.Width:Species, data=iris)
glm1 <- glm(Sepal.Length~Sepal.Width+Species+Sepal.Width:Species, data=iris,
            family=quasipoisson("identity"))
cm <- rbind( lambda1 = c(1,0,1,0,0,0),
             lambda2 = c(1,0,0,1,0,0))
estimable(lm1,cm)
estimable(glm1,cm)