summary method for a mixturecure object fit using curegmifs,
cureem, cv_curegmifs, or cv_cureem.
Usage
# S3 method for class 'mixturecure'
summary(object, ...)Value
prints the number of non-zero coefficients from the incidence and
latency portions of the fitted mixture cure model when using the minimum AIC
to select the final model. When fitting a model using curegmifs or
cureem the summary function additionally prints results associated
with the following model selection methods: the step and value that maximizes
the log-likelihood; the step and value that minimizes the AIC, modified AIC
(mAIC), corrected AIC (cAIC), BIC, modified BIC (mBIC), and extended BIC
(EBIC). This information can be used to guide the user in the selection of
a final model from the solution path.
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
)
summary(fit)
#> Mixture cure model fit using the GMIFS algorithm
#> Number of non-zero incidence covariates at minimum AIC: 11
#> Number of non-zero latency covariates at minimum AIC: 12
#> Optimal step for selected information criterion: GMIFS algorithm
#> at step = 1280 logLik = -8.82938729096585
#> at step = 990 AIC = 70.4578620870773
#> at step = 3 mAIC = 173.247514591857
#> at step = 683 cAIC = 94.9501288908126
#> at step = 683 BIC = 126.569843615684
#> at step = 3 mBIC = 164.040539742741
#> at step = 684 EBIC = -Inf