| predict.survreg {survival} | R Documentation |
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),...)
object |
result of a model fit using the survreg function.
|
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 ("linear", with "lp" as an allowed abbreviation),
a predicted quantile on the original scale of the data ("quantile"),
a quantile on the linear predictor scale ("uquantile"),
or the matrix of terms for the linear predictor ("terms").
At this time "link" and linear predictor ("lp") are identical.
|
se.fit |
if TRUE, include the standard errors of the prediction in the result. |
terms |
subset of terms. The default for residual type "terms" is a matrix with
one column for every term (excluding the intercept) in the model.
|
p |
vector of percentiles. This is used only for quantile predictions. |
... |
other arguments |
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
data(stanford2)
fit <- survreg(Surv(time,status) ~ age + age^2, data=stanford2,
dist='lognormal')
plot(stanford2$age, stanford2$time, xlab='Age', ylab='Days',
xlim=c(0,65), ylim=c(.01, 10^6), log='y')
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)