R/5_3_compare_by_calibrate.R
compare_by_calibrate.Rd
Compare highdimensional Cox models by model calibration
compare_by_calibrate(x, time, event, model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", "snet"), method = c("fitting", "bootstrap", "cv", "repeated.cv"), boot.times = NULL, nfolds = NULL, rep.times = NULL, pred.at, ngroup = 5, seed = 1001, trace = TRUE)
x  Matrix of training data used for fitting the model; on which to run the calibration. 

time  Survival time.
Must be of the same length with the number of rows as 
event  Status indicator, normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as 
model.type  Model types to compare. Could be at least two of

method  Calibration method.
Could be 
boot.times  Number of repetitions for bootstrap. 
nfolds  Number of folds for crossvalidation and repeated crossvalidation. 
rep.times  Number of repeated times for repeated crossvalidation. 
pred.at  Time point at which calibration should take place. 
ngroup  Number of groups to be formed for calibration. 
seed  A random seed for crossvalidation fold division. 
trace  Logical. Output the calibration progress or not.
Default is 
data(smart) x < as.matrix(smart[, c(1, 2)]) time < smart$TEVENT event < smart$EVENT # Compare lasso and adaptive lasso by 5fold crossvalidation cmp.cal.cv < compare_by_calibrate( x, time, event, model.type = c("lasso", "alasso"), method = "fitting", pred.at = 365 * 9, ngroup = 5, seed = 1001 )#> Starting model 1 : lasso #> Start fitting ... #> Starting model 2 : alasso #> Start fitting ...print(cmp.cal.cv)#> HighDimensional Cox Model Calibration Object #> Random seed: 1001 #> Calibration method: fitting #> Model type: lasso #> glmnet model alpha: 1 #> glmnet model lambda: 0.01345781 #> glmnet model penalty factor: not specified #> Calibration time point: 3285 #> Number of groups formed for calibration: 5 #> #> HighDimensional Cox Model Calibration Object #> Random seed: 1001 #> Calibration method: fitting #> Model type: alasso #> glmnet model alpha: 1 #> glmnet model lambda: 0.05055676 #> glmnet model penalty factor: specified #> Calibration time point: 3285 #> Number of groups formed for calibration: 5 #>summary(cmp.cal.cv)#> Model type: lasso #> Calibration Summary Table #> Predicted Observed Lower 95% Upper 95% #> 1 0.5787402 0.4712308 0.3991153 0.5563768 #> 2 0.7028772 0.6932137 0.5743867 0.8366233 #> 3 0.7617722 0.8156289 0.7703284 0.8635934 #> 4 0.8046333 0.8140415 0.7148020 0.9270589 #> 5 0.8479692 0.9049829 0.8728166 0.9383345 #> #> Model type: alasso #> Calibration Summary Table #> Predicted Observed Lower 95% Upper 95% #> 1 0.5490816 0.4759925 0.4014625 0.5643587 #> 2 0.7193136 0.7182540 0.6502817 0.7933313 #> 3 0.7934671 0.8176343 0.7636014 0.8754906 #> 4 0.8341466 0.8326544 0.7378270 0.9396693 #> 5 0.8728679 0.9196582 0.8876358 0.9528358 #>plot(cmp.cal.cv)