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## $Id: estimable.R 1333 2009-05-09 05:00:47Z warnes $
estimable <- function (obj, cm, beta0, conf.int=NULL, show.beta0, ...)
{
UseMethod("estimable")
}
estimable.default <- function (obj, cm, beta0, conf.int=NULL,
show.beta0, joint.test=FALSE, ...)
{
if (is.matrix(cm) || is.data.frame(cm))
{
cm <- t(apply(cm, 1, .to.est, obj=obj))
}
else if(is.list(cm))
{
cm <- matrix(.to.est(obj, cm), nrow=1)
}
else if(is.vector(cm))
{
cm <- matrix(.to.est(obj, cm), nrow=1)
}
else
{
stop("`cm' argument must be of type vector, list, or matrix.")
}
if(missing(show.beta0))
{
if(!missing(beta0))
show.beta0=TRUE
else
show.beta0=FALSE
}
if (missing(beta0))
{
beta0 = rep(0, ifelse(is.null(nrow(cm)), 1, nrow(cm)))
}
if (joint.test==TRUE)
{
.wald(obj, cm, beta0)
}
else
{
if ("lme" %in% class(obj)) {
stat.name <- "t.stat"
cf <- summary(obj)$tTable
rho <- summary(obj)$cor
vcv <- rho * outer(cf[, 2], cf[, 2])
tmp <- cm
tmp[tmp==0] <- NA
df.all <- t(abs(t(tmp) * obj$fixDF$X))
df <- apply(df.all, 1, min, na.rm=TRUE)
problem <- apply(df.all !=df, 1, any, na.rm=TRUE)
if (any(problem))
warning(paste("Degrees of freedom vary among parameters used to ",
"construct linear contrast(s): ",
paste((1:nrow(tmp))[problem], collapse=", "),
". Using the smallest df among the set of parameters.",
sep=""))
}
else if ("lm" %in% class(obj))
{
stat.name <- "t.stat"
cf <- summary.lm(obj)$coefficients
vcv <- summary.lm(obj)$cov.unscaled * summary.lm(obj)$sigma^2
df <- obj$df.residual
if ("glm" %in% class(obj))
{
vcv <- summary(obj)$cov.scaled
if(family(obj)[1] %in% c("poisson", "binomial"))
{
stat.name <- "X2.stat"
df <- 1
}
else
{
stat.name <- "t.stat"
df <- obj$df.residual
}
}
}
else if ("geese" %in% class(obj))
{
stat.name <- "X2.stat"
cf <- summary(obj)$mean
vcv <- obj$vbeta
df <- 1
}
else if ("gee" %in% class(obj))
{
stat.name <- "X2.stat"
cf <- summary(obj)$coef
vcv <- obj$robust.variance
df <- 1
}
else
{
stop("obj must be of class 'lm', 'glm', 'aov', 'lme', 'gee', 'geese' or 'nlme'")
}
if (is.null(cm))
cm <- diag(dim(cf)[1])
if (!dim(cm)[2]==dim(cf)[1])
stop(paste("\n Dimension of ", deparse(substitute(cm)),
": ", paste(dim(cm), collapse="x"),
", not compatible with no of parameters in ",
deparse(substitute(obj)), ": ", dim(cf)[1], sep=""))
ct <- cm %*% cf[, 1]
ct.diff <- cm %*% cf[, 1] - beta0
vc <- sqrt(diag(cm %*% vcv %*% t(cm)))
if (is.null(rownames(cm)))
rn <- paste("(", apply(cm, 1, paste, collapse=" "),
")", sep="")
else rn <- rownames(cm)
switch(stat.name,
t.stat={
prob <- 2 * (1 - pt(abs(ct.diff/vc), df))
},
X2.stat={
prob <- 1 - pchisq((ct.diff/vc)^2, df=1)
})
if (stat.name=="X2.stat")
{
retval <- cbind(hyp=beta0, est=ct, stderr=vc,
"X^2 value"=(ct.diff/vc)^2,
df=df, prob=1 - pchisq((ct.diff/vc)^2, df=1))
dimnames(retval) <- list(rn, c("beta0", "Estimate", "Std. Error",
"X^2 value", "DF", "Pr(>|X^2|)"))
}
else if (stat.name=="t.stat")
{
retval <- cbind(hyp=beta0, est=ct, stderr=vc, "t value"=ct.diff/vc,
df=df, prob=2 * (1 - pt(abs(ct.diff/vc), df)))
dimnames(retval) <- list(rn, c("beta0", "Estimate", "Std. Error",
"t value", "DF", "Pr(>|t|)"))
}
if (!is.null(conf.int))
{
if (conf.int <=0 || conf.int >=1)
stop("conf.int should be between 0 and 1. Usual values are 0.95, 0.90")
alpha <- 1 - conf.int
switch(stat.name,
t.stat={
quant <- qt(1 - alpha/2, df)
},
X2.stat={
quant <- qt(1 - alpha/2, 100)
})
nm <- c(colnames(retval), "Lower.CI", "Upper.CI")
retval <- cbind(retval, lower=ct.diff - vc * quant, upper=ct.diff +
vc * quant)
colnames(retval) <- nm
}
rownames(retval) <- make.unique(rownames(retval))
retval <- as.data.frame(retval)
if(!show.beta0) retval$beta0 <- NULL
return(retval)
}
}
.wald <- function (obj, cm,
beta0=rep(0, ifelse(is.null(nrow(cm)), 1, nrow(cm))))
{
if (!is.matrix(cm) && !is.data.frame(cm))
cm <- matrix(cm, nrow=1)
df <- nrow(cm)
if ("geese" %in% class(obj))
{
cf <- obj$beta
vcv <- obj$vbeta
}
else if ("gee" %in% class(obj))
{
cf <- summary(obj)$coef
vcv <- obj$robust.variance
}
else if ("lm" %in% class(obj))
{
cf <- summary.lm(obj)$coefficients[, 1]
if ("glm" %in% class(obj))
vcv <- summary(obj)$cov.scaled
else
vcv <- summary.lm(obj)$cov.unscaled * summary.lm(obj)$sigma^2
}
else if ("lme" %in% class(obj))
{
s.o <- summary(obj)
cf <- s.o$tTable[,1]
se <- s.o$tTable[, 2]
rho <- s.o$cor
vcv <- rho * outer(se, se)
}
else
stop("obj must be of class 'lm', 'glm', 'aov', 'gee', 'geese', or 'lme'.")
u <- (cm %*% cf)-beta0
vcv.u <- cm %*% vcv %*% t(cm)
W <- t(u) %*% solve(vcv.u) %*% u
prob <- 1 - pchisq(W, df=df)
retval <- as.data.frame(cbind(W, df, prob))
names(retval) <- c("X2.stat", "DF", "Pr(>|X^2|)")
print(as.data.frame(retval))
}
estimable.mer <- function (obj, cm, beta0, conf.int=NULL, show.beta0,
sim.mer=TRUE, n.sim=1000, ...)
{
if (is.matrix(cm) || is.data.frame(cm))
{
cm <- t(apply(cm, 1, .to.est, obj=obj))
}
else if(is.list(cm))
{
cm <- matrix(.to.est(obj, cm), nrow=1)
}
else if(is.vector(cm))
{
cm <- matrix(.to.est(obj, cm), nrow=1)
}
else
{
stop("'cm' argument must be of type vector, list, or matrix.")
}
if(missing(show.beta0))
{
if(!missing(beta0))
show.beta0=TRUE
else
show.beta0=FALSE
}
if (missing(beta0))
{
beta0 = rep(0, ifelse(is.null(nrow(cm)), 1, nrow(cm)))
}
if ("mer" %in% class(obj)) {
if(sim.mer)
return(est.mer(obj=obj, cm=cm, beta0=beta0, conf.int=conf.int,
show.beta0=show.beta0, n.sim=n.sim))
stat.name <- "mer"
cf <- as.matrix(fixef(obj))
vcv <- as.matrix(vcov(obj))
df <- NA
}
else {
stop("obj is not of class mer")
}
if (is.null(rownames(cm)))
rn <- paste("(", apply(cm, 1, paste, collapse=" "),
")", sep="")
else rn <- rownames(cm)
ct <- cm %*% cf[, 1]
ct.diff <- cm %*% cf[, 1] - beta0
vc <- sqrt(diag(cm %*% vcv %*% t(cm)))
retval <- cbind(hyp=beta0, est=ct, stderr=vc, "t value"=ct.diff/vc)
dimnames(retval) <- list(rn, c("beta0", "Estimate", "Std. Error",
"t value"))
rownames(retval) <- make.unique(rownames(retval))
retval <- as.data.frame(retval)
if(!show.beta0) retval$beta0 <- NULL
return(retval)
}
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