estimable {gmodels} | R Documentation |
Compute and test contrasts and other estimable linear functions of model coefficients for for lm, glm, lme, and geese objects
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))))
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=beta0 is performed. Otherwise 'row-wise' tests are performed, i.e. (L %*% beta)[i]=beta0[i] |
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. |
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.
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.
The estimated fixed effect parameter of lme
objects may have
different degrees of freedom. If a specified contrast includes
nonzero coefficients for parameters with differing degrees of freedom,
the smallest number of degrees of freedom is used and a warning
message is issued.
BXC (Bendix Carstensen) bxc@novonordisk.com, Gregory R. Warnes Gregory_R_Warnes@groton.pfizer.com, and Søren Højsgaard sorenh@agrsci.dk
fit.contrast
,
lm
, lme
,
contrasts
,
contrast
,
# 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)