validate {hdnom} | R Documentation |
Validate high-dimensional Cox models with time-dependent AUC
validate(x, time, event, model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", "snet"), alpha, lambda, pen.factor = NULL, gamma, lambda1, lambda2, method = c("bootstrap", "cv", "repeated.cv"), boot.times = NULL, nfolds = NULL, rep.times = NULL, tauc.type = c("CD", "SZ", "UNO"), tauc.time, seed = 1001, trace = TRUE)
x |
Matrix of training data used for fitting the model; on which to run the validation. |
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 type to validate. Could be one of |
alpha |
Value of the elastic-net mixing parameter alpha for
|
lambda |
Value of the penalty parameter lambda to use in the model fits on the resampled data. From the fitted Cox model. |
pen.factor |
Penalty factors to apply to each coefficient. From the fitted adaptive lasso or adaptive elastic-net model. |
gamma |
Value of the model parameter gamma for MCP/SCAD/Mnet/Snet models. |
lambda1 |
Value of the penalty parameter lambda1 for fused lasso model. |
lambda2 |
Value of the penalty parameter lambda2 for fused lasso model. |
method |
Validation method.
Could be |
boot.times |
Number of repetitions for bootstrap. |
nfolds |
Number of folds for cross-validation and repeated cross-validation. |
rep.times |
Number of repeated times for repeated cross-validation. |
tauc.type |
Type of time-dependent AUC.
Including |
tauc.time |
Numeric vector. Time points at which to evaluate the time-dependent AUC. |
seed |
A random seed for resampling. |
trace |
Logical. Output the validation progress or not.
Default is |
Chambless, L. E. and G. Diao (2006). Estimation of time-dependent area under the ROC curve for long-term risk prediction. Statistics in Medicine 25, 3474–3486.
Song, X. and X.-H. Zhou (2008). A semiparametric approach for the covariate specific ROC curve with survival outcome. Statistica Sinica 18, 947–965.
Uno, H., T. Cai, L. Tian, and L. J. Wei (2007). Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 102, 527–537.
data(smart) x <- as.matrix(smart[, -c(1, 2)])[1:500, ] time <- smart$TEVENT[1:500] event <- smart$EVENT[1:500] y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) # Model validation by bootstrap with time-dependent AUC # Normally boot.times should be set to 200 or more, # we set it to 3 here only to save example running time. val.boot <- validate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "bootstrap", boot.times = 3, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, seed = 1010 ) # Model validation by 5-fold cross-validation with time-dependent AUC val.cv <- validate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "cv", nfolds = 5, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, seed = 1010 ) # Model validation by repeated cross-validation with time-dependent AUC val.repcv <- validate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "repeated.cv", nfolds = 5, rep.times = 3, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, seed = 1010 ) # bootstrap-based discrimination curves has a very narrow band print(val.boot) summary(val.boot) plot(val.boot) # k-fold cv provides a more strict evaluation than bootstrap print(val.cv) summary(val.cv) plot(val.cv) # repeated cv provides similar results as k-fold cv # but more robust than k-fold cv print(val.repcv) summary(val.repcv) plot(val.repcv) # # Test fused lasso, SCAD, and Mnet models # # data(smart) # x = as.matrix(smart[, -c(1, 2)])[1:500,] # time = smart$TEVENT[1:500] # event = smart$EVENT[1:500] # y = survival::Surv(time, event) # # set.seed(1010) # val.boot = validate( # x, time, event, model.type = "flasso", # lambda1 = 5, lambda2 = 2, # method = "bootstrap", boot.times = 10, # tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, # seed = 1010) # # val.cv = validate( # x, time, event, model.type = "scad", # gamma = 3.7, alpha = 1, lambda = 0.05, # method = "cv", nfolds = 5, # tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, # seed = 1010) # # val.repcv = validate( # x, time, event, model.type = "mnet", # gamma = 3, alpha = 0.3, lambda = 0.05, # method = "repeated.cv", nfolds = 5, rep.times = 3, # tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, # seed = 1010) # # print(val.boot) # summary(val.boot) # plot(val.boot) # # print(val.cv) # summary(val.cv) # plot(val.cv) # # print(val.repcv) # summary(val.repcv) # plot(val.repcv)