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rcspline.plot <- function(x, y, model=c("logistic","cox","ols"), xrange,
event, nk=5, knots=NULL, show=c("xbeta", "prob"),
adj=NULL, xlab, ylab, ylim, plim=c(0,1),
plotcl=TRUE, showknots=TRUE, add=FALSE, subset,
lty=1, noprint=FALSE, m, smooth=FALSE, bass=1,
main="auto", statloc)
{
model <- match.arg(model)
show <- match.arg(show)
if(! missing(event))
model<-"cox"
if(model == "cox" & missing(event))
stop('event must be given for model="cox"')
if(show == "prob" & ! missing(adj))
stop('show="prob" cannot be used with adj')
if(show == "prob" & model != "logistic")
stop('show="prob" can only be used with model="logistic"')
if(length(x) != length(y))
stop('x and y must have the same length')
if(! missing(event) && length(event) != length(y))
stop('y and event must have the same length')
if(! missing(adj)) {
if(! is.matrix(adj)) adj <- as.matrix(adj)
if(dim(adj)[1] != length(x))
stop('x and adj must have the same length')
}
if(missing(xlab))
xlab <- label(x)
if(missing(ylab))
ylab <- label(y)
isna <- is.na(x) | is.na(y)
if(! missing(event))
isna <- isna | is.na(event)
nadj <- 0
if(! missing(adj)) {
nadj <- ncol(adj)
isna <- isna | apply(is.na(adj), 1, sum) > 0
}
if(! missing(subset))
isna <- isna | (! subset)
x <- x[! isna]
y <- y[! isna]
if(! missing(event))
event <- event[! isna]
if(! missing(adj))
adj <- adj[! isna, ]
n <- length(x)
if(n<6)
stop('fewer than 6 non-missing observations')
if(missing(xrange)) {
frac<-10./max(n, 200)
xrange<-quantile(x, c(frac, 1.-frac))
}
if(missing(knots))
xx <- rcspline.eval(x, nk=nk)
else xx <- rcspline.eval(x, knots)
knots <- attr(xx, "knots")
nk <- length(knots)
df1 <- nk-2
if(model == "logistic") {
if (!requireNamespace("rms", quietly = TRUE))
stop("The 'logistic' model requires the 'rms' package.")
b <- rms::lrm.fit(cbind(x, xx, adj), y)
beta <- b$coef
cov <- vcov(b)
model.lr <- b$stats["Model L.R."]
offset <- 1 #to skip over intercept parameter
ylabl <-
if(show == "prob")
"Probability"
else "log Odds"
sampled <- paste("Logistic Regression Model, n=", n," d=", sum(y), sep="")
}
if(model == "cox") {
sRequire('survival')
##11mar04
## added coxph.control around iter.max, eps 11mar04
lllin <- survival::coxph.fit(cbind(x, adj), cbind(y, event), strata=NULL,
offset=NULL, init=NULL,
control=survival::coxph.control(iter.max=10, eps=.0001),
method="efron", rownames=NULL)$loglik[2]
b <- survival::coxph.fit(cbind(x, xx, adj), cbind(y, event), strata=NULL,
offset=NULL, init=NULL,
control=survival::coxph.control(iter.max=10, eps=.0001),
method="efron", rownames=NULL)
beta <- b$coef
if(! noprint) {
print(beta);
print(b$loglik)
}
beta <- b$coef
cov <- vcov(b)
model.lr <- 2*(b$loglik[2]-b$loglik[1])
offset <- 0
ylabl <- "log Relative Hazard"
sampled <- paste("Cox Regression Model, n=",n," events=",sum(event),
sep="")
}
if(model == "logistic" | model == "cox") {
model.df <- nk - 1 + nadj
model.aic <- model.lr-2.*model.df
v <- solve(cov[(1 + offset) : (nk + offset - 1), (1 + offset) : (nk + offset - 1)])
assoc.chi <- beta[(1 + offset) : (nk + offset - 1)] %*% v %*%
beta[(1 + offset) : (nk + offset - 1)]
assoc.df <- nk - 1 #attr(v,"rank")
assoc.p <- 1.-pchisq(assoc.chi, nk - 1)
v <- solve(cov[(2 + offset) : (nk + offset - 1), (2 + offset) : (nk + offset - 1)])
linear.chi <- beta[(2 + offset) : (nk + offset - 1)] %*% v %*%
beta[(2 + offset) : (nk + offset - 1)]
linear.df <- nk - 2 #attr(v,"rank")
linear.p <- 1. - pchisq(linear.chi, linear.df)
if(nadj > 0) {
ntot <- offset + nk - 1 + nadj
v <- solve(cov[(nk + offset) : ntot, (nk + offset) : ntot])
adj.chi <- beta[(nk + offset) : ntot] %*% v %*%
beta[(nk + offset) : ntot]
adj.df <- ncol(v) #attr(v,"rank")
adj.p <- 1. - pchisq(adj.chi, adj.df)
} else {
adj.chi <- 0
adj.p <- 0
}
}
## Evaluate xbeta for expanded x at desired range
xe <- seq(xrange[1], xrange[2], length=600)
if(model == "cox")
xx <- rcspline.eval(xe, knots, inclx=TRUE)
else
xx<- cbind(rep(1, length(xe)), rcspline.eval(xe, knots, inclx=TRUE))
xbeta <- xx %*% beta[1 : (nk - 1 + offset)]
var <- drop(((xx %*% cov[1 : (nk - 1 + offset), 1 : (nk - 1 + offset)])*xx) %*%
rep(1, ncol(xx)))
lower <- xbeta - 1.96*sqrt(var)
upper <- xbeta + 1.96*sqrt(var)
if(show == "prob") {
xbeta <- 1./(1. + exp(-xbeta))
lower <- 1./(1. + exp(-lower))
upper <- 1./(1. + exp(-upper))
}
xlim <- range(pretty(xe))
if(missing(ylim))
ylim <- range(pretty(c(xbeta, if(plotcl) lower, if(plotcl) upper)))
if(main == "auto") {
if(show == "xbeta")
main <- "Estimated Spline Transformation"
else main <- "Spline Estimate of Prob{Y=1}"
}
if(! interactive() & missing(statloc))
statloc<-"ll"
if(! add) {
oldmar<-par("mar")
if(! missing(statloc) && statloc[1] == "ll")
oldmar[1]<- 11
oldpar <- par(err= - 1, mar=oldmar)
plot(xe, xbeta, type="n", main=main, xlab=xlab, ylab=ylabl,
xlim=xlim, ylim=ylim)
lines(xe, xbeta, lty=lty)
ltext<-function(z, line, label, cex=.8, adj=0)
{
zz<-z
zz$y<-z$y-(line - 1)*1.2*cex*par("csi")*(par("usr")[4]-par("usr")[3])/
(par("fin")[2]) #was 1.85
text(zz, label, cex=cex, adj=adj)
}
sl<-0
if(missing(statloc)) {
cat("Click left mouse button at upper left corner for statistics\n")
z<-locator(1)
statloc<-"l"
} else if(statloc[1] != "none") {
if(statloc[1] == "ll") {
z<-list(x=par("usr")[1], y=par("usr")[3])
sl<-3
} else z<-list(x=statloc[1], y=statloc[2])
}
if(statloc[1] != "none" & (model == "logistic" | model == "cox")) {
rnd <- function(x, r=2) as.single(round(x, r))
ltext(z, 1 + sl, sampled)
ltext(z, 2 + sl, " Statistic X2 df")
chistats<-format(as.single(round(c(model.lr, model.aic,
assoc.chi, linear.chi, adj.chi), 2)))
pvals<-format(as.single(round(c(assoc.p, linear.p, adj.p), 4)))
ltext(z, 3 + sl, paste("Model L.R. ", chistats[1], model.df,
" AIC=", chistats[2]))
ltext(z, 4 + sl, paste("Association Wald ", chistats[3], assoc.df,
" p= ", pvals[1]))
ltext(z, 5 + sl, paste("Linearity Wald ", chistats[4], linear.df,
" p= ", pvals[2]))
if(nadj > 0)ltext(z, 6 + sl, paste("Adjustment Wald " , chistats[5],
adj.df, " p= ", pvals[3]))}
} else lines(xe, xbeta, lty=lty)
if(plotcl) {
prn(cbind(xe, lower, upper))
lines(xe, lower, lty=2)
lines(xe, upper, lty=2)
}
if(showknots) {
bot.arrow <- par("usr")[3]
top.arrow <- bot.arrow + .05 * (par("usr")[4]-par("usr")[3])
for(i in 1 : nk)
arrows(knots[i], top.arrow, knots[i], bot.arrow, length=.1)
}
if(model == "logistic" & nadj == 0) {
if(smooth) {
z<-supsmu(x, y, bass=bass)
if(show == "xbeta") z$y <- logb(z$y/(1.-z$y))
points(z, cex=.4)
}
if(! missing(m)) {
z<-groupn(x, y, m=m)
if(show == "xbeta") z$y <- logb(z$y/(1.-z$y))
points(z, pch=2, mkh=.05)}
}
if(! add)
par(oldpar)
invisible(list(knots=knots, x=xe, xbeta=xbeta, lower=lower, upper=upper))
}
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