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ols <- function(formula, data, weights, subset, na.action=na.delete,
method = "qr", model = FALSE, x = FALSE, y = FALSE,
se.fit=FALSE, linear.predictors=TRUE,
penalty=0, penalty.matrix, tol=1e-7, sigma=NULL,
var.penalty=c('simple','sandwich'), ...){
call <- match.call()
var.penalty <- match.arg(var.penalty)
m <- match.call(expand = FALSE)
m$method <- m$model <- m$x <- m$y <- m$se.fit <-
m$linear.predictors <- m$penalty <-
m$penalty.matrix <- m$tol <- m$sigma <- m$var.penalty <- m$... <- NULL
m$na.action <- na.action
if(.R.) m$drop.unused.levels <- TRUE
m[[1]] <- as.name("model.frame")
##X's present)
if(length(attr(terms(formula),"term.labels"))) {
## R's model.frame.default gives wrong model frame if [.factor
## removes unused factor levels
if(.R.) {
dul <- .Options$drop.unused.levels
if(!length(dul) || dul) {
on.exit(options(drop.unused.levels=dul))
options(drop.unused.levels=FALSE)
}
}
X <- Design(eval(m, sys.parent()))
if(.R.) options(drop.unused.levels=dul)
atrx <- attributes(X)
atr <- atrx$Design
nact <- atrx$na.action
Terms <- atrx$terms
assig <- DesignAssign(atr, 1, Terms)
penpres <- !(missing(penalty) && missing(penalty.matrix))
if(penpres && missing(var.penalty))
warning('default for var.penalty has changed to "simple"')
if(method == "model.frame") return(X)
scale <- as.character(formula[2])
attr(Terms, "formula") <- formula
if(length(nact$nmiss)) {
jia <- grep('%ia%',names(nact$nmiss), fixed=TRUE)
if(length(jia)) nact$nmiss <- nact$nmiss[-jia]
names(nact$nmiss) <-
c(scale,atr$name[atr$assume.code!=9])
}
weights <- model.extract(X, weights)
if(length(weights) && penpres)
stop('may not specify penalty with weights')
Y <- model.extract(X, response)
n <- length(Y)
if(model) m <- X
X <- model.matrix(Terms, X)
if(length(atr$colnames))
dimnames(X)[[2]] <- c("Intercept",atr$colnames)
else dimnames(X)[[2]] <- c("Intercept",dimnames(X)[[2]][-1])
if(method=="model.matrix") return(X) }
##Model with no covariables:
else {
if(length(weights))
stop('weights not implemented when no covariables are present')
assig <- NULL
yy <- attr(terms(formula),"variables")[1]
Y <- eval(yy,sys.parent(2))
nmiss <- sum(is.na(Y))
if(nmiss==0) nmiss <- NULL else names(nmiss) <- as.character(yy)
Y <- Y[!is.na(Y)]
yest <- mean(Y)
coef <- yest
n <- length(Y)
if(!length(sigma)) sigma <- sqrt(sum((Y-yest)^2)/(n-1))
cov <- matrix(sigma*sigma/n, nrow=1, ncol=1,
dimnames=list("Intercept","Intercept"))
fit <- list(coefficients=coef, var=cov,
non.slopes=1, fail=FALSE, residuals=Y-yest,
df.residual=n-1, intercept=TRUE)
if(linear.predictors) {
fit$linear.predictors <- rep(yest,n);
names(fit$linear.predictors) <- names(Y)
}
if(model) fit$model <- m
if(x) fit$x <- matrix(1, ncol=1, nrow=n,
dimnames=list(NULL,"Intercept"))
if(y) fit$y <- Y
fit$fitFunction <- c('ols','lm')
oldClass(fit) <- if(.SV4.)"Design" else c("ols","Design","lm")
return(fit)
}
if(!penpres) {
fit <- if(length(weights))
lm.wfit(X, Y, weights, method=method, ...) else
lm.fit(X, Y, method=method, ...)
if(.R.) cov.unscaled <- chol2inv(fit$qr$qr) else {
rinv <- solve(fit$R, diag(length(fit$coefficients)))
cov.unscaled <- rinv %*% t(rinv)
}
r <- fit$residuals
if(length(weights)) { ## see summary.lm
sse <- sum(weights * r^2)
yhat <- Y - r
m <- sum(weights * yhat / sum(weights))
ssr <- sum(weights * (yhat - m)^2)
r2 <- ssr / (ssr + sse)
if(!length(sigma)) sigma <- sqrt(sse/fit$df.residual)
} else {
sse <- sum(fit$residuals^2)
if(!length(sigma)) sigma <- sqrt(sse/fit$df.residual)
r2 <- 1-sse/sum((Y-mean(Y))^2)
}
fit$var <- sigma*sigma*cov.unscaled
cnam <- dimnames(X)[[2]]
dimnames(fit$var) <- list(cnam, cnam)
fit$stats <- c(n=n,'Model L.R.'=-n*logb(1-r2),
'd.f.'=length(fit$coef)-1,R2=r2,Sigma=sigma)
} else {
p <- length(atr$colnames)
if(missing(penalty.matrix)) penalty.matrix <- Penalty.matrix(atr,X)
if(nrow(penalty.matrix)!=p || ncol(penalty.matrix)!=p)
stop('penalty matrix does not have',p,'rows and columns')
psetup <- Penalty.setup(atr, penalty)
penalty <- psetup$penalty
multiplier <- psetup$multiplier
if(length(multiplier)==1) penalty.matrix <- multiplier*penalty.matrix
else {
a <- diag(sqrt(multiplier))
penalty.matrix <- a %*% penalty.matrix %*% a
}
fit <- lm.pfit(X, Y,
penalty.matrix=penalty.matrix, tol=tol,
var.penalty=var.penalty)
fit$penalty <- penalty
}
if(model)
fit$model <- m
if(linear.predictors) {
fit$linear.predictors <- Y-fit$residuals
names(fit$linear.predictors) <- names(Y)
}
if(x)
fit$x <- X
if(y)
fit$y <- Y
if(se.fit) {
se <- drop((((X %*% fit$var) * X) %*% rep(1, ncol(X)))^0.5)
if(!.R.) storage.mode(se) <- "single"
names(se) <- names(Y)
fit$se.fit <- se
}
fit <- c(fit, list(call=call, terms=Terms, Design=atr,
non.slopes=1, na.action=nact,
scale.pred=scale, fail=FALSE,
fitFunction=c('ols','lm')))
fit$assign <- assig
oldClass(fit) <- if(.SV4.)'Design' else c("ols","Design","lm")
fit
}
lm.pfit <- function(X, Y, penalty.matrix, tol=1e-7, regcoef.only=FALSE,
var.penalty=c('simple','sandwich')) {
var.penalty <- match.arg(var.penalty)
p <- ncol(X)-1
pm <- rbind(matrix(0, ncol=p+1, nrow=1),
cbind(matrix(0, ncol=1, nrow=p), penalty.matrix))
xpx <- t(X) %*% X
Z <- solvet(xpx+pm, tol=tol)
coef <- Z %*% t(X) %*% Y
if(regcoef.only) return(list(coefficients=coef))
res <- drop(Y - X %*% coef)
n <- length(Y)
sse <- sum(res^2)
s2 <- drop( (sse + t(coef) %*% pm %*% coef) / n )
var <- if(var.penalty=='simple') s2 * Z else s2 * Z %*% xpx %*% Z
cnam <- dimnames(X)[[2]]
dimnames(var) <- list(cnam, cnam)
sst <- sum((Y-mean(Y))^2)
lr <- n*(1+logb(sst/n))-n*logb(s2)-sse/s2
s2.unpen <- sse/n
dag <- diag((xpx / s2.unpen) %*% (s2 * Z))
df <- sum(dag) - 1
stats <- c(n=n, 'Model L.R.'=lr, 'd.f.'=df, R2=1-sse/sst, Sigma=sqrt(s2))
list(coefficients=drop(coef), var=var, residuals=res, df.residual=n-1,
penalty.matrix=penalty.matrix,
stats=stats, effective.df.diagonal=dag)
}
predict.ols <-
function(object, newdata,
type=c("lp","x","data.frame","terms","adjto","adjto.data.frame",
"model.frame"),
se.fit=FALSE, conf.int=FALSE, conf.type=c('mean','individual'),
incl.non.slopes, non.slopes, kint=1,
na.action=na.keep, expand.na=TRUE, center.terms=TRUE, ...)
predictDesign(object, newdata, type, se.fit, conf.int, conf.type,
incl.non.slopes, non.slopes, kint,
na.action, expand.na, center.terms, ...)
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