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lrm.s
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lrm.s
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lrm <- function(formula, data=environment(formula),
subset, na.action=na.delete,
method="lrm.fit", model=FALSE, x=FALSE, y=FALSE,
linear.predictors=TRUE, se.fit=FALSE,
penalty=0, penalty.matrix, tol=1e-7, strata.penalty=0,
var.penalty=c('simple','sandwich'),
weights, normwt=FALSE, scale=FALSE, ...)
{
call <- match.call()
var.penalty <- match.arg(var.penalty)
callenv <- parent.frame() # don't delay these evaluations
weights <- if(! missing(weights)) eval(substitute(weights), data, callenv)
subset <- if(! missing(subset )) eval(substitute(subset), data, callenv)
data <-
modelData(data, formula,
weights=weights, subset=subset,
na.action=na.action, callenv=callenv)
tform <- terms(formula, specials='strat', data=data)
nstrata <- 1
if(length(atl <- attr(tform, "term.labels")) &&
any(atl!=".")) { ##X's present
X <- Design(data, formula=formula, specials='strat')
atrx <- attributes(X)
sformula <- atrx$sformula
nact <- atrx$na.action
if(method=="model.frame") return(X)
Terms <- atrx$terms
attr(Terms, "formula") <- formula
atr <- atrx$Design
mmcolnames <- atr$mmcolnames
Y <- model.extract(X, 'response')
offs <- atrx$offset
if(!length(offs)) offs <- 0
weights <- wt <- model.extract(X, 'weights')
if(length(weights))
warning('currently weights are ignored in model validation and bootstrapping lrm fits')
if(model) m <- X
stra <- attr(tform, 'specials')$strat
Strata <- NULL
Terms.ns <- Terms
if(length(stra)) {
temp <- untangle.specials(Terms.ns, 'strat', 1)
Terms.ns <- Terms.ns[-temp$terms]
attr(Terms, "factors") <- pmin(attr(Terms,"factors"),1)
attr(Terms.ns,"factors") <- pmin(attr(Terms.ns,"factors"),1)
Strata <- X[[stra]]
nstrata <- length(levels(Strata))
}
X <- model.matrix(Terms.ns, X)
alt <- attr(mmcolnames, 'alt')
if(! all(mmcolnames %in% colnames(X)) && length(alt)) mmcolnames <- alt
## prn(colnames(X)); prn(mmcolnames)
X <- X[, mmcolnames, drop=FALSE]
colnames(X) <- atr$colnames
xpres <- length(X) > 0
p <- length(atr$colnames)
n <- length(Y)
penpres <- !(missing(penalty) && missing(penalty.matrix))
if(penpres && missing(var.penalty))
warning('default for var.penalty has changed to "simple"')
if(!penpres) penalty.matrix <- matrix(0,ncol=p,nrow=p) else {
if(missing(penalty.matrix)) penalty.matrix <- Penalty.matrix(atr, X) else
if(nrow(penalty.matrix)!=p || ncol(penalty.matrix)!=p) stop(
paste("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
}
}
}
else {
# X <- eval.parent(m)
X <- Design(data, formula=formula, specials='strat')
offs <- model.offset(X)
if(! length(offs)) offs <- 0
Y <- model.extract(X, 'response')
Y <- Y[!is.na(Y)]
Terms <- X <- NULL
xpres <- FALSE
penpres <- FALSE
penalty.matrix <- NULL
} ##Model: y~. without data= -> no predictors
if(method == "model.matrix") return(X)
if(! is.factor(Y)) Y <- as.vector(Y) # in case Y is a matrix
if(nstrata > 1) {
if(scale) stop('scale=TRUE not implemented for stratified model')
f <- lrm.fit.strat(X, Y, Strata, offset=offs,
penalty.matrix=penalty.matrix,
strata.penalty=strata.penalty,
tol=tol,
weights=weights, normwt=normwt, ...)
}
else {
if(existsFunction(method)) {
fitter <- getFunction(method)
f <- fitter(X, Y, offset=offs,
penalty.matrix=penalty.matrix, tol=tol,
weights=weights, normwt=normwt, scale=scale, ...)
}
else stop(paste("unimplemented method:", method))
}
if(f$fail) {
warning("Unable to fit model using ", dQuote(method))
return(f)
}
f$call <- NULL
if(model) f$model <- m
if(x) {
f$x <- X
f$strata <- Strata
}
if(y) f$y <- Y
nrp <- f$non.slopes
if(penpres) {
f$penalty <- penalty
if(nstrata == 1) {
## Get improved covariance matrix
v <- f$var
if(var.penalty=='sandwich') f$var.from.info.matrix <- v
f.nopenalty <-
fitter(X, Y, offset=offs, initial=f$coef, maxit=1, tol=tol,
scale=scale)
## info.matrix.unpenalized <- solvet(f.nopenalty$var, tol=tol)
info.matrix.unpenalized <- f.nopenalty$info.matrix
dag <- diag(info.matrix.unpenalized %*% v)
f$effective.df.diagonal <- dag
f$var <- if(var.penalty == 'simple') v else
v %*% info.matrix.unpenalized %*% v
df <- sum(dag[-(1:nrp)])
lr <- f.nopenalty$stats["Model L.R."]
pval <- 1 - pchisq(lr, df)
f$stats[c('d.f.','Model L.R.','P')] <- c(df, lr, pval)
}
}
ass <- if(xpres) DesignAssign(atr, nrp, Terms) else list()
if(xpres) {
if(linear.predictors) names(f$linear.predictors) <- names(Y)
else
f$linear.predictors <- NULL
if(se.fit) {
if(nstrata > 1) stop('se.fit=T not implemented for strat')
xint <- matrix(0, nrow=length(Y), ncol=f$non.slopes)
xint[,1] <- 1
X <- cbind(xint, X)
se <- drop((((X %*% f$var) * X) %*% rep(1, ncol(X)))^.5)
names(se) <- names(Y)
f$se.fit <- se
}
}
f <- c(f, list(call=call, Design=if(xpres)atr,
scale.pred=c("log odds","Odds Ratio"),
terms=Terms, assign=ass, na.action=nact, fail=FALSE,
interceptRef=1,
nstrata=nstrata, sformula=sformula))
class(f) <- c("lrm","rms","glm")
f
}
print.lrm <- function(x, digits=4, r2=c(0,2,4), strata.coefs=FALSE, coefs=TRUE,
pg=FALSE, title='Logistic Regression Model', ...) {
latex <- prType() == 'latex'
z <- list()
k <- 0
if(length(x$freq) > 3) {
k <- k + 1
z[[k]] <- list(type='print', list(x$freq),
title='Frequencies of Responses')
}
if(length(x$sumwty)) {
k <- k + 1
z[[k]] <- list(type='print', list(x$sumwty),
title='Sum of Weights by Response Category')
}
if(!is.null(x$nmiss)) { ## for backward compatibility
k <- k + 1
z[[k]] <- list(type='print', list(x$nmiss),
title='Frequencies of Missing Values Due to Each Variable')
}
else if(length(x$na.action)) {
k <- k + 1
z[[k]] <- list(type=paste('naprint',class(x$na.action),sep='.'),
list(x$na.action))
}
ns <- x$non.slopes
nstrata <- x$nstrata
if(!length(nstrata)) nstrata <- 1
pm <- x$penalty.matrix
penaltyFactor <- NULL
if(length(pm)) {
psc <- if(length(pm)==1) sqrt(pm)
else
sqrt(diag(pm))
penalty.scale <- c(rep(0,ns),psc)
cof <- matrix(x$coef[-(1:ns)], ncol=1)
k <- k + 1
z[[k]] <- list(type='print', list(as.data.frame(x$penalty, row.names='')),
title='Penalty factors')
penaltyFactor <- as.vector(t(cof) %*% pm %*% cof)
}
## ?ok to have uncommented next 3 lines?
est.exp <- 1:ns
if(length(x$est)) est.exp <-
c(est.exp, ns + x$est[x$est + ns <= length(x$coefficients)])
vv <- diag(x$var)
cof <- x$coef
if(strata.coefs) {
cof <- c(cof, x$strata.coef)
vv <- c(vv, vcov(x))
## TODO: implement in vcov:
## vv <- c(vv, vcov(x, which='strata.var.diag'))
if(length(pm)) penalty.scale <- c(penalty.scale, rep(NA, x$nstrata - 1))
}
score.there <- nstrata==1 && (length(x$est) < length(x$coef) - ns)
stats <- x$stats
maxd <- stats['Max Deriv']
ci <- x$clusterInfo
misc <- reListclean(Obs =stats['Obs'],
'Sum of weights'=stats['Sum of Weights'],
Strata=if(nstrata > 1) nstrata,
'Cluster on' = ci$name,
'Clusters' = ci$n,
'max |deriv|' = maxd)
if(length(x$freq) < 4) {
names(x$freq) <- paste(if(latex)'~~' else ' ',
names(x$freq), sep='')
misc <- c(misc[1], x$freq, misc[-1])
}
lr <- reListclean('LR chi2' = stats['Model L.R.'],
'd.f.' = round(stats['d.f.'], 3),
'Pr(> chi2)' = stats['P'],
Penalty = penaltyFactor,
dec = c(2,NA,-4,2))
newr2 <- grepl('R2\\(', names(stats))
disc <- reListclean(R2 = if(0 %in% r2) stats['R2'],
namesFrom = if(any(newr2)) stats[newr2][setdiff(r2, 0)],
g = if(pg) stats['g'],
gr = if(pg) stats['gr'],
gp = if(pg) stats['gp'],
Brier = stats['Brier'],
dec = 3)
discr <-reListclean(C = stats['C'],
Dxy = stats['Dxy'],
gamma = stats['Gamma'],
'tau-a' = stats['Tau-a'],
dec = 3)
headings <- c('','Model Likelihood\nRatio Test',
'Discrimination\nIndexes',
'Rank Discrim.\nIndexes')
data <- list(misc, lr, disc, discr)
k <- k + 1
z[[k]] <- list(type='stats', list(headings=headings, data=data))
if(coefs) {
k <- k + 1
z[[k]] <- list(type='coefmatrix',
list(coef=cof, se=sqrt(vv),
aux=if(length(pm)) penalty.scale,
auxname='Penalty Scale'))
}
if(score.there) {
q <- (1:length(cof))[-est.exp]
if(length(q)==1) vv <- x$var[q,q] else vv <- diag(x$var[q,q])
Z <- x$u[q]/sqrt(vv)
stats <- cbind(format(Z,digits=2), format(1-pchisq(Z^2,1),digits=4))
dimnames(stats) <- list(names(cof[q]),c("Score Z","P"))
k <- k + 1
z[[k]] <- list(type='print', list(stats))
}
prModFit(x, title=title, z, digits=digits,
coefs=coefs, ...)
}