Calibrate high-dimensional Cox models
calibrate( 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("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 x.event: Status indicator, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x.model.type: Model type to calibrate. Could be one of "lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", or "snet".alpha: Value of the elastic-net mixing parameter alpha for enet, aenet, mnet, and snet models. For lasso, alasso, mcp, and scad models, please set alpha = 1. alpha=1: lasso (l1) penalty; alpha=0: ridge (l2) penalty. Note that for mnet and snet models, alpha can be set to very close to 0 but not 0 exactly.lambda: Value of the penalty parameter lambda to use in the model fits on the resampled data. From the Cox model you have built.pen.factor: Penalty factors to apply to each coefficient. From the built 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: Calibration method. Options including "fitting", "bootstrap", "cv", and "repeated.cv".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.pred.at: Time point at which calibration should take place.ngroup: Number of groups to be formed for calibration.seed: A random seed for resampling.trace: Logical. Output the calibration progress or not. Default is TRUE.data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) # Fit Cox model with lasso penalty fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) # Model calibration by fitting the original data directly cal.fitting <- calibrate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "fitting", pred.at = 365 * 9, ngroup = 5, seed = 1010 ) # Model calibration by 5-fold cross-validation cal.cv <- calibrate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "cv", nfolds = 5, pred.at = 365 * 9, ngroup = 5, seed = 1010 ) print(cal.fitting) summary(cal.fitting) plot(cal.fitting) print(cal.cv) summary(cal.cv) plot(cal.cv) # # 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) # cal.fitting = calibrate( # x, time, event, model.type = "flasso", # lambda1 = 5, lambda2 = 2, # method = "fitting", # pred.at = 365 * 9, ngroup = 5, # seed = 1010) # # cal.boot = calibrate( # x, time, event, model.type = "scad", # gamma = 3.7, alpha = 1, lambda = 0.03, # method = "bootstrap", boot.times = 10, # pred.at = 365 * 9, ngroup = 5, # seed = 1010) # # cal.cv = calibrate( # x, time, event, model.type = "mnet", # gamma = 3, alpha = 0.3, lambda = 0.03, # method = "cv", nfolds = 5, # pred.at = 365 * 9, ngroup = 5, # seed = 1010) # # cal.repcv = calibrate( # x, time, event, model.type = "flasso", # lambda1 = 5, lambda2 = 2, # method = "repeated.cv", nfolds = 5, rep.times = 3, # pred.at = 365 * 9, ngroup = 5, # seed = 1010) # # print(cal.fitting) # summary(cal.fitting) # plot(cal.fitting) # # print(cal.boot) # summary(cal.boot) # plot(cal.boot) # # print(cal.cv) # summary(cal.cv) # plot(cal.cv) # # print(cal.repcv) # summary(cal.repcv) # plot(cal.repcv)
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