View source: R/predict.flexsurvreg.R
predict.flexsurvreg  R Documentation 
Predict outcomes from flexible survival models at the covariate values
specified in newdata
.
## S3 method for class 'flexsurvreg'
predict(
object,
newdata,
type = "response",
times,
start = 0,
conf.int = FALSE,
conf.level = 0.95,
se.fit = FALSE,
p = c(0.1, 0.9),
...
)
object 
Output from 
newdata 
Data frame containing covariate values at which to produce
fitted values. There must be a column for every covariate in the model
formula used to fit If 
type 
Character vector for the type of predictions desired.

times 
Vector of time horizons at which to compute fitted values.
Only applies when If not specified, predictions for For 
start 
Optional lefttruncation time or times. The returned
survival, hazard, or cumulative hazard will be conditioned on survival up
to this time. 
conf.int 
Logical. Should confidence intervals be returned?
Default is 
conf.level 
Width of symmetric confidence intervals, relative to 1. 
se.fit 
Logical. Should standard errors of fitted values be returned?
Default is 
p 
Vector of quantiles at which to return fitted values when

... 
Not currently used. 
A tibble
with same number of rows as
newdata
and in the same order. If multiple predictions are
requested, a tibble
containing a single listcolumn
of data frames.
For the listcolumn of data frames  the dimensions of each data frame
will be identical. Rows are added for each value of times
or
p
requested.
This function is a wrapper around summary.flexsurvreg
,
designed to help flexsurv to integrate with the "tidymodels"
ecosystem, in particular the censored package.
summary.flexsurvreg
returns the same results but in a more
conventional format.
Matthew T. Warkentin (https://github.com/mattwarkentin)
summary.flexsurvreg
,
residuals.flexsurvreg
fitg < flexsurvreg(formula = Surv(futime, fustat) ~ age, data = ovarian, dist = "gengamma")
## Simplest prediction: mean or median, for covariates defined by original dataset
predict(fitg)
predict(fitg, type = "quantile", p = 0.5)
## Simple prediction for userdefined covariate values
predict(fitg, newdata = data.frame(age = c(40, 50, 60)))
predict(fitg, type = "quantile", p = 0.5, newdata = data.frame(age = c(40,50,60)))
## Predict multiple quantiles and unnest
require(tidyr)
pr < predict(fitg, type = "survival", times = c(600, 800))
tidyr::unnest(pr, .pred)
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