Predicted Values for a ‘survreg’ Object
Predicted values for a survreg object
## S3 method for class 'survreg'
predict(object, newdata,
type=c("response", "link", "lp", "linear", "terms", "quantile",
"uquantile"),
se.fit=FALSE, terms=NULL, p=c(0.1, 0.9), na.action=na.pass, ...)object |
result of a model fit using the |
newdata |
data for prediction. If absent predictions are for the subjects used in the original fit. |
type |
the type of predicted value.
This can be on the original scale of the data (response),
the linear predictor ( |
se.fit |
if |
terms |
subset of terms. The default for residual type |
p |
vector of percentiles. This is used only for quantile predictions. |
na.action |
applies only when the |
... |
for future methods |
a vector or matrix of predicted values.
Escobar and Meeker (1992). Assessing influence in regression analysis with censored data. Biometrics, 48, 507-528.
# Draw figure 1 from Escobar and Meeker, 1992.
fit <- survreg(Surv(time,status) ~ age + I(age^2), data=stanford2,
dist='lognormal')
with(stanford2, plot(age, time, xlab='Age', ylab='Days',
xlim=c(0,65), ylim=c(.1, 10^5), log='y', type='n'))
with(stanford2, points(age, time, pch=c(2,4)[status+1], cex=.7))
pred <- predict(fit, newdata=list(age=1:65), type='quantile',
p=c(.1, .5, .9))
matlines(1:65, pred, lty=c(2,1,2), col=1)
# Predicted Weibull survival curve for a lung cancer subject with
# ECOG score of 2
lfit <- survreg(Surv(time, status) ~ ph.ecog, data=lung)
pct <- 1:98/100 # The 100th percentile of predicted survival is at +infinity
ptime <- predict(lfit, newdata=data.frame(ph.ecog=2), type='quantile',
p=pct, se=TRUE)
matplot(cbind(ptime$fit, ptime$fit + 2*ptime$se.fit,
ptime$fit - 2*ptime$se.fit)/30.5, 1-pct,
xlab="Months", ylab="Survival", type='l', lty=c(1,2,2), col=1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.