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

Source: R/predict.R
predict.mixturecure.Rd

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$training
fit <- curegmifs(Surv(Time, Censor) ~ .,
  data = training, x_latency = training,
  model = "weibull", thresh = 1e-4, maxit = 2000,
  epsilon = 0.01, verbose = FALSE
)
predict_train <- predict(fit)
names(predict_train)
#> [1] "p_uncured"      "linear_latency" "latency_risk"  
testing <- temp$testing
predict_test <- predict(fit, newdata = testing)