predict.mixturecure: Predicted probabilities for susceptibles, linear predictor...

In hdcuremodels: High-Dimensional Cure Models

Predicted probabilities for susceptibles, linear predictor for latency, and risk class for latency for mixture cure fit

Description

This function returns a list that includes the predicted probabilities for susceptibles as well as the linear predictor for the latency distribution and a dichotomous risk for latency for a curegmifs, cureem, cv_curegmifs or cv_cureem fitted object.

Usage

## S3 method for class 'mixturecure'
predict(object, newdata, model_select = "AIC", ...)

Arguments

object	a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, or cv_cureem.
newdata	an optional data.frame that minimally includes the incidence and/or latency variables to use for predicting the response. If omitted, the training data are used.
model_select	either a case-sensitive parameter for models fit using curegmifs or cureem or any numeric step along the solution path can be selected. The default is model_select = "AIC" which calculates the predicted values using the coefficients from the model achieving the minimum AIC. The complete list of options are: "AIC" for the minimum AIC (default). "mAIC" for the minimum modified AIC. "cAIC" for the minimum corrected AIC. "BIC", for the minimum BIC. "mBIC" for the minimum modified BIC. "EBIC" for the minimum extended BIC. "logLik" for the step that maximizes the log-likelihood. n where n is any numeric value from the solution path. This option has no effect for objects fit using cv_curegmifs or cv_cureem.
...	other arguments

Value

p_uncured	a vector of probabilities from the incidence portion of the fitted model representing the P(uncured).
linear_latency	a vector for the linear predictor from the latency portion of the model.
latency_risk	a dichotomous class representing low (below the median) versus high risk for the latency portion of the model.

See Also

curegmifs, cureem, coef.mixturecure, summary.mixturecure, plot.mixturecure

Examples

library(survival)
withr::local_seed(1234)
temp <- generate_cure_data(n = 100, j = 10, n_true = 10, a = 1.8)
training <- temp$training
fit <- curegmifs(Surv(Time, Censor) ~ .,
  data = training, x_latency = training,
  model = "weibull", thresh = 1e-4, maxit = 2000,
  epsilon = 0.01, verbose = FALSE
)
predict_train <- predict(fit)
names(predict_train)
testing <- temp$testing
predict_test <- predict(fit, newdata = testing)

hdcuremodels documentation built on Aug. 8, 2025, 7:38 p.m.