coef.mixturecure: Extract model coefficients from a fitted mixturecure object

In hdcuremodels: High-Dimensional Cure Models

Extract model coefficients from a fitted mixturecure object

Description

coef.mixturecure is a generic function which extracts the model coefficients from a fitted mixturecure model object fit using curegmifs, cureem, cv_curegmifs, or cv_cureem.

Usage

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

Arguments

object	a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, or cv_cureem.
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

rate	estimated rate parameter when fitting a Weibull or exponential mixture cure model.
shape	estimated shape parameter when fitting a Weibull mixture cure model.
b0	estimated intercept for the incidence portion of the mixture cure model.
beta_inc	the vector of coefficient estimates for the incidence portion of the mixture cure model.
beta_lat	the vector of coefficient estimates for the latency portion of the mixture cure model.
p_uncured	a vector of probabilities from the incidence portion of the fitted model representing the P(uncured).

See Also

curegmifs, cureem, summary.mixturecure, plot.mixturecure, predict.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
)
coef(fit)

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