predict.survreg {survival5} | R Documentation |
Predicted values for a survreg
object
predict.survreg(object, newdata, type=c("response", "link", "linear", "response", "terms", "quantile", "uquantile"), se.fit=F, terms=labels.lm(object), p=c(0.1, 0.9), ripley=F)
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 typeterms 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. |
ripley |
temporary arg. Compute the standard errors of quantile predictions in the way shown in an a draft of Ripley and Venables, i.e., partially ignoring the variance in the estimate of scale. |
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)