predict.survival_ln_mixture: Predict from a Lognormal Mixture Model

In lnmixsurv: Bayesian Mixture Log-Normal Survival Model

Predict from a Lognormal Mixture Model

Description

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)
# Wrong way of doing
set.seed(1)
mod <- survival_ln_mixture(Surv(time, status == 2) ~ factor(sex), lung, intercept = TRUE)

## Not run: 
# this piece of code will throw error
predict(mod, data.frame(sex = 1), type = "survival", eval_time = 100)

## End(Not run)

# Correct way
lung$sex <- factor(lung$sex) # converting to factor before
set.seed(1)
mod2 <- survival_ln_mixture(Surv(time, status == 2) ~ sex, lung, intercept = TRUE)
# Note: the categorical predictors must be a character.
predict(mod2, data.frame(sex = "1"), type = "survival", eval_time = 100)

lnmixsurv documentation built on Sept. 11, 2024, 7:18 p.m.