Computes predicted survival probabilities or hazards and optionally confidence
limits (for survival only) for parametric survival models fitted with
`psm`

.
If getting predictions for more than one observation, `times`

must
be specified. For a model without predictors, no input data are
specified.

```
# S3 method for psm
survest(fit, newdata, linear.predictors, x, times, fun,
loglog=FALSE, conf.int=0.95,
what=c("survival","hazard","parallel"), …)
```# S3 method for survest.psm
print(x, …)

fit

fit from `psm`

newdata, linear.predictors, x, times, conf.int

see `survest.cph`

. One of `newdata`

, `linear.predictors`

, `x`

must be given.
`linear.predictors`

includes the intercept.
If `times`

is omitted, predictions are made at 200 equally spaced points
between 0 and the maximum failure/censoring time used to fit the model.

`x`

can also be a result from `survest.psm`

.

what

The default is to compute survival probabilities. Set `what="hazard"`

or
some abbreviation of `"hazard"`

to compute hazard rates.
`what="parallel"`

assumes that the length of `times`

is the number of
subjects (or one), and causes `survest`

to estimate the
\(i^{th}\) subject's survival probability at the \(i^{th}\) value of
`times`

(or at the scalar value of `times`

).
`what="parallel"`

is used by `val.surv`

for example.

loglog

set to `TRUE`

to transform survival estimates and confidence limits using
log-log

fun

a function to transform estimates and optional confidence intervals

…

unused

see `survest.cph`

. If the model has no predictors, predictions are
made with respect to varying time only, and the returned object
is of class `"npsurv"`

so the survival curve can be plotted
with `survplot.npsurv`

. If `times`

is omitted, the
entire survival curve or hazard from `t=0,…,fit$maxtime`

is estimated, with
increments computed to yield 200 points where `fit$maxtime`

is the
maximum survival time in the data used in model fitting. Otherwise,
the `times`

vector controls the time points used.

Confidence intervals are based on asymptotic normality of the linear predictors. The intervals account for the fact that a scale parameter may have been estimated jointly with beta.

`psm`

, `survreg`

, `rms`

, `survfit`

, `predictrms`

, `survplot`

,
`survreg.distributions`

# NOT RUN { # Simulate data from a proportional hazards population model n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" cens <- 15*runif(n) h <- .02*exp(.04*(age-50)) dt <- -log(runif(n))/h label(dt) <- 'Follow-up Time' e <- ifelse(dt <= cens,1,0) dt <- pmin(dt, cens) units(dt) <- "Year" S <- Surv(dt,e) f <- psm(S ~ lsp(age,c(40,70))) survest(f, data.frame(age=seq(20,80,by=5)), times=2) #Get predicted survival curve for 40 year old survest(f, data.frame(age=40)) #Get hazard function for 40 year old survest(f, data.frame(age=40), what="hazard")$surv #still called surv # }