# survest.psm

##### Parametric Survival Estimates

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.

- Keywords
- models, regression, survival

##### Usage

```
# 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, …)

##### Arguments

- 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

##### Details

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.

##### Value

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.

##### See Also

`psm`

, `survreg`

, `rms`

, `survfit`

, `predictrms`

, `survplot`

,
`survreg.distributions`

##### Examples

```
# 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
# }
```

*Documentation reproduced from package rms, version 5.1-3.1, License: GPL (>= 2)*