Predict from a Lognormal Mixture Model

Usage

# S3 method for class 'survival_ln_mixture'
predict(
  object,
  new_data,
  type,
  eval_time,
  interval = "none",
  level = 0.95,
  ...
)

Arguments

object

A survival_ln_mixture object.

new_data

A data frame or matrix of new predictors.

type

A single character. The type of predictions to generate. Valid options are:

"time" for the survival time. not implmeented
"survival" for the survival probability.
"hazard" for the hazard.

eval_time

For type = "hazard" or type = "survival", the times for the distribution.

interval

should interval estimates be added? Options are "none" and "credible".

level

the tail area of the intervals. Default value is 0.95.

...

Not used, but required for extensibility.

Value

A tibble of predictions. The number of rows in the tibble is guaranteed to be the same as the number of rows in new_data.

Note

Categorical predictors must be converted to factors before the fit, otherwise the predictions will fail.

Examples


# Categorical variables must be converted to factor before the fit.
require(survival)
set.seed(1)
mod <- survival_ln_mixture(Surv(time, status == 2) ~ factor(sex), lung, intercept = TRUE)
# Would result in error
if (FALSE) { # \dontrun{
predict(mod, data.frame(sex = 1), type = "survival", eval_time = 100)
} # }

# Correct way
lung$sex <- factor(lung$sex)
set.seed(1)
mod2 <- survival_ln_mixture(Surv(time, status == 2) ~ sex, lung, intercept = TRUE)
# Note: the categorical predictors must be character.
predict(mod2, data.frame(sex = "1"), type = "survival", eval_time = 100)
#> # A tibble: 1 × 2
#>   .pred            strata
#>   <list>           <fct> 
#> 1 <tibble [1 × 2]> sex=1