,
Qing-Song Xu [aut],
Miao-Zhu Li [aut],
Frank Harrell [ctb] (rms author),
Sergej Potapov [ctb] (survAUC author),
Werner Adler [ctb] (survAUC author),
Matthias Schmid [ctb] (survAUC author)| Title: | Benchmarking and Visualization Toolkit for Penalized Cox Models |
|---|---|
| Description: | Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. |
| Authors: | Nan Xiao [aut, cre] ,
Qing-Song Xu [aut],
Miao-Zhu Li [aut],
Frank Harrell [ctb] (rms author),
Sergej Potapov [ctb] (survAUC author),
Werner Adler [ctb] (survAUC author),
Matthias Schmid [ctb] (survAUC author) |
| Maintainer: | Nan Xiao <[email protected]> |
| License: | GPL-3 | file LICENSE |
| Version: | 6.0.4 |
| Built: | 2024-09-05 05:15:47 UTC |
| Source: | https://github.com/nanxstats/hdnom |
Construct nomograms ojects for high-dimensional Cox models
as_nomogram( object, x, time, event, pred.at = NULL, fun.at = NULL, funlabel = NULL )as_nomogram( object, x, time, event, pred.at = NULL, fun.at = NULL, funlabel = NULL )
object |
Model object fitted by 'hdnom::fit_*()' functions. |
x |
Matrix of training data used for fitting the model. |
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 |
pred.at |
Time point at which to plot nomogram prediction axis. |
fun.at |
Function values to label on axis. |
funlabel |
Label for |
The nomogram visualizes the model under the automatically selected "optimal" hyperparameters (e.g. lambda, alpha, gamma).
data(smart) x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) print(nom) plot(nom)data(smart) x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) print(nom) plot(nom)
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 )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 |
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 calibrate. 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 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 |
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 |
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)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)
Externally calibrate high-dimensional Cox models
calibrate_external( object, x, time, event, x_new, time_new, event_new, pred.at, ngroup = 5 )calibrate_external( object, x, time, event, x_new, time_new, event_new, pred.at, ngroup = 5 )
object |
Model object fitted by |
x |
Matrix of training data used for fitting the model. |
time |
Survival time of the training data.
Must be of the same length with the number of rows as |
event |
Status indicator of the training data,
normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |
x_new |
Matrix of predictors for the external validation data. |
time_new |
Survival time of the external validation data.
Must be of the same length with the number of rows as |
event_new |
Status indicator of the external validation data,
normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |
pred.at |
Time point at which external calibration should take place. |
ngroup |
Number of groups to be formed for external calibration. |
library("survival") # Load imputed SMART data data(smart) # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 5 ) print(cal.ext) summary(cal.ext) plot(cal.ext, xlim = c(0.6, 1), ylim = c(0.6, 1)) # # Test fused lasso, MCP, and Snet models # data(smart) # # Use first 500 samples as training data # # (the data used for internal validation) # x <- as.matrix(smart[, -c(1, 2)])[1:500, ] # time <- smart$TEVENT[1:500] # event <- smart$EVENT[1:500] # # # Take 1000 samples as external validation data. # # In practice, usually use data collected in other studies. # x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] # time_new <- smart$TEVENT[1001:2000] # event_new <- smart$EVENT[1001:2000] # # flassofit <- fit_flasso(x, survival::Surv(time, event), nfolds = 5, seed = 11) # scadfit <- fit_mcp(x, survival::Surv(time, event), nfolds = 5, seed = 11) # mnetfit <- fit_snet(x, survival::Surv(time, event), nfolds = 5, seed = 11) # # cal.ext1 <- calibrate_external( # flassofit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # cal.ext2 <- calibrate_external( # scadfit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # cal.ext3 <- calibrate_external( # mnetfit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # print(cal.ext1) # summary(cal.ext1) # plot(cal.ext1) # # print(cal.ext2) # summary(cal.ext2) # plot(cal.ext2) # # print(cal.ext3) # summary(cal.ext3) # plot(cal.ext3)library("survival") # Load imputed SMART data data(smart) # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 5 ) print(cal.ext) summary(cal.ext) plot(cal.ext, xlim = c(0.6, 1), ylim = c(0.6, 1)) # # Test fused lasso, MCP, and Snet models # data(smart) # # Use first 500 samples as training data # # (the data used for internal validation) # x <- as.matrix(smart[, -c(1, 2)])[1:500, ] # time <- smart$TEVENT[1:500] # event <- smart$EVENT[1:500] # # # Take 1000 samples as external validation data. # # In practice, usually use data collected in other studies. # x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] # time_new <- smart$TEVENT[1001:2000] # event_new <- smart$EVENT[1001:2000] # # flassofit <- fit_flasso(x, survival::Surv(time, event), nfolds = 5, seed = 11) # scadfit <- fit_mcp(x, survival::Surv(time, event), nfolds = 5, seed = 11) # mnetfit <- fit_snet(x, survival::Surv(time, event), nfolds = 5, seed = 11) # # cal.ext1 <- calibrate_external( # flassofit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # cal.ext2 <- calibrate_external( # scadfit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # cal.ext3 <- calibrate_external( # mnetfit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # print(cal.ext1) # summary(cal.ext1) # plot(cal.ext1) # # print(cal.ext2) # summary(cal.ext2) # plot(cal.ext2) # # print(cal.ext3) # summary(cal.ext3) # plot(cal.ext3)
Compare high-dimensional 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 )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 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 cross-validation 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 5-fold cross-validation cmp.cal.cv <- compare_by_calibrate( x, time, event, model.type = c("lasso", "alasso"), method = "fitting", pred.at = 365 * 9, ngroup = 5, seed = 1001 ) print(cmp.cal.cv) summary(cmp.cal.cv) plot(cmp.cal.cv)data(smart) x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT # Compare lasso and adaptive lasso by 5-fold cross-validation cmp.cal.cv <- compare_by_calibrate( x, time, event, model.type = c("lasso", "alasso"), method = "fitting", pred.at = 365 * 9, ngroup = 5, seed = 1001 ) print(cmp.cal.cv) summary(cmp.cal.cv) plot(cmp.cal.cv)
Compare high-dimensional Cox models by model validation
compare_by_validate( x, time, event, model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", "snet"), 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 )compare_by_validate( x, time, event, model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", "snet"), 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 types to compare. Could be at least two
of |
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 cross-validation fold division. |
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)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Compare lasso and adaptive lasso by 5-fold cross-validation cmp.val.cv <- compare_by_validate( x, time, event, model.type = c("lasso", "alasso"), method = "cv", nfolds = 5, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, seed = 1001 ) print(cmp.val.cv) summary(cmp.val.cv) plot(cmp.val.cv) plot(cmp.val.cv, interval = TRUE)data(smart) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Compare lasso and adaptive lasso by 5-fold cross-validation cmp.val.cv <- compare_by_validate( x, time, event, model.type = c("lasso", "alasso"), method = "cv", nfolds = 5, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, seed = 1001 ) print(cmp.val.cv) summary(cmp.val.cv) plot(cmp.val.cv) plot(cmp.val.cv, interval = TRUE)
Automatic model selection for high-dimensional Cox models with adaptive elastic-net penalty, evaluated by penalized partial-likelihood.
fit_aenet( x, y, nfolds = 5L, alphas = seq(0.05, 0.95, 0.05), rule = c("lambda.min", "lambda.1se"), seed = c(1001, 1002), parallel = FALSE )fit_aenet( x, y, nfolds = 5L, alphas = seq(0.05, 0.95, 0.05), rule = c("lambda.min", "lambda.1se"), seed = c(1001, 1002), parallel = FALSE )
x |
Data matrix. |
y |
Response matrix made with |
nfolds |
Fold numbers of cross-validation. |
alphas |
Alphas to tune in |
rule |
Model selection criterion, |
seed |
Two random seeds for cross-validation fold division in two estimation steps. |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) # To enable parallel parameter tuning, first run: # library("doParallel") # registerDoParallel(detectCores()) # then set fit_aenet(..., parallel = TRUE). fit <- fit_aenet( x, y, nfolds = 3, alphas = c(0.3, 0.7), rule = "lambda.1se", seed = c(5, 7) ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) # To enable parallel parameter tuning, first run: # library("doParallel") # registerDoParallel(detectCores()) # then set fit_aenet(..., parallel = TRUE). fit <- fit_aenet( x, y, nfolds = 3, alphas = c(0.3, 0.7), rule = "lambda.1se", seed = c(5, 7) ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with adaptive lasso penalty, evaluated by penalized partial-likelihood.
fit_alasso( x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"), seed = c(1001, 1002) )fit_alasso( x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"), seed = c(1001, 1002) )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
rule |
Model selection criterion, |
seed |
Two random seeds for cross-validation fold division in two estimation steps. |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_alasso(x, y, nfolds = 3, rule = "lambda.1se", seed = c(7, 11)) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_alasso(x, y, nfolds = 3, rule = "lambda.1se", seed = c(7, 11)) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with elastic-net penalty, evaluated by penalized partial-likelihood.
fit_enet( x, y, nfolds = 5L, alphas = seq(0.05, 0.95, 0.05), rule = c("lambda.min", "lambda.1se"), seed = 1001, parallel = FALSE )fit_enet( x, y, nfolds = 5L, alphas = seq(0.05, 0.95, 0.05), rule = c("lambda.min", "lambda.1se"), seed = 1001, parallel = FALSE )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
alphas |
Alphas to tune in |
rule |
Model selection criterion, |
seed |
A random seed for cross-validation fold division. |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) # To enable parallel parameter tuning, first run: # library("doParallel") # registerDoParallel(detectCores()) # then set fit_enet(..., parallel = TRUE). fit <- fit_enet( x, y, nfolds = 3, alphas = c(0.3, 0.7), rule = "lambda.1se", seed = 11 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) # To enable parallel parameter tuning, first run: # library("doParallel") # registerDoParallel(detectCores()) # then set fit_enet(..., parallel = TRUE). fit <- fit_enet( x, y, nfolds = 3, alphas = c(0.3, 0.7), rule = "lambda.1se", seed = 11 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with fused lasso penalty, evaluated by cross-validated likelihood.
fit_flasso( x, y, nfolds = 5L, lambda1 = c(0.001, 0.05, 0.5, 1, 5), lambda2 = c(0.001, 0.01, 0.5), maxiter = 25, epsilon = 0.001, seed = 1001, trace = FALSE, parallel = FALSE, ... )fit_flasso( x, y, nfolds = 5L, lambda1 = c(0.001, 0.05, 0.5, 1, 5), lambda2 = c(0.001, 0.01, 0.5), maxiter = 25, epsilon = 0.001, seed = 1001, trace = FALSE, parallel = FALSE, ... )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
lambda1 |
Vector of lambda1 candidates.
Default is |
lambda2 |
Vector of lambda2 candidates.
Default is |
maxiter |
The maximum number of iterations allowed.
Default is |
epsilon |
The convergence criterion.
Default is |
seed |
A random seed for cross-validation fold division. |
trace |
Output the cross-validation parameter tuning
progress or not. Default is |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
... |
The cross-validation procedure used in this function is the
approximated cross-validation provided by the penalized
package. Be careful dealing with the results since they might be more
optimistic than a traditional CV procedure. This cross-validation
method is more suitable for datasets with larger number of observations,
and a higher number of cross-validation folds.
data("smart") x <- as.matrix(smart[, -c(1, 2)])[1:120, ] time <- smart$TEVENT[1:120] event <- smart$EVENT[1:120] y <- survival::Surv(time, event) fit <- fit_flasso( x, y, lambda1 = c(1, 10), lambda2 = c(0.01), nfolds = 3, seed = 11 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)])[1:120, ] time <- smart$TEVENT[1:120] event <- smart$EVENT[1:120] y <- survival::Surv(time, event) fit <- fit_flasso( x, y, lambda1 = c(1, 10), lambda2 = c(0.01), nfolds = 3, seed = 11 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with lasso penalty, evaluated by penalized partial-likelihood.
fit_lasso(x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"), seed = 1001)fit_lasso(x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"), seed = 1001)
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
rule |
Model selection criterion, |
seed |
A random seed for cross-validation fold division. |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with MCP penalty, evaluated by penalized partial-likelihood.
fit_mcp( x, y, nfolds = 5L, gammas = c(1.01, 1.7, 3, 100), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )fit_mcp( x, y, nfolds = 5L, gammas = c(1.01, 1.7, 3, 100), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
gammas |
Gammas to tune in |
eps |
Convergence threshhold. |
max.iter |
Maximum number of iterations. |
seed |
A random seed for cross-validation fold division. |
trace |
Output the cross-validation parameter tuning
progress or not. Default is |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_mcp(x, y, nfolds = 3, gammas = c(2.1, 3), seed = 1001) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_mcp(x, y, nfolds = 3, gammas = c(2.1, 3), seed = 1001) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with Mnet penalty, evaluated by penalized partial-likelihood.
fit_mnet( x, y, nfolds = 5L, gammas = c(1.01, 1.7, 3, 100), alphas = seq(0.05, 0.95, 0.05), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )fit_mnet( x, y, nfolds = 5L, gammas = c(1.01, 1.7, 3, 100), alphas = seq(0.05, 0.95, 0.05), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
gammas |
Gammas to tune in |
alphas |
Alphas to tune in |
eps |
Convergence threshhold. |
max.iter |
Maximum number of iterations. |
seed |
A random seed for cross-validation fold division. |
trace |
Output the cross-validation parameter tuning
progress or not. Default is |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_mnet( x, y, nfolds = 3, gammas = 3, alphas = c(0.3, 0.6, 0.9), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_mnet( x, y, nfolds = 3, gammas = 3, alphas = c(0.3, 0.6, 0.9), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with SCAD penalty, evaluated by penalized partial-likelihood.
fit_scad( x, y, nfolds = 5L, gammas = c(2.01, 2.3, 3.7, 200), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )fit_scad( x, y, nfolds = 5L, gammas = c(2.01, 2.3, 3.7, 200), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
gammas |
Gammas to tune in |
eps |
Convergence threshhold. |
max.iter |
Maximum number of iterations. |
seed |
A random seed for cross-validation fold division. |
trace |
Output the cross-validation parameter tuning
progress or not. Default is |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_scad( x, y, nfolds = 3, gammas = c(3.7, 5), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_scad( x, y, nfolds = 3, gammas = c(3.7, 5), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Automatic model selection for high-dimensional Cox models with Snet penalty, evaluated by penalized partial-likelihood.
fit_snet( x, y, nfolds = 5L, gammas = c(2.01, 2.3, 3.7, 200), alphas = seq(0.05, 0.95, 0.05), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )fit_snet( x, y, nfolds = 5L, gammas = c(2.01, 2.3, 3.7, 200), alphas = seq(0.05, 0.95, 0.05), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE )
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
gammas |
Gammas to tune in |
alphas |
Alphas to tune in |
eps |
Convergence threshhold. |
max.iter |
Maximum number of iterations. |
seed |
A random seed for cross-validation fold division. |
trace |
Output the cross-validation parameter tuning
progress or not. Default is |
parallel |
Logical. Enable parallel parameter tuning or not,
default is |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_snet( x, y, nfolds = 3, gammas = 3.7, alphas = c(0.3, 0.8), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_snet( x, y, nfolds = 3, gammas = 3.7, alphas = c(0.3, 0.8), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)
Derived from peperr:::basesurv and gbm::basehaz.gbm.
glmnet_basesurv(time, event, lp, times.eval = NULL, centered = FALSE)glmnet_basesurv(time, event, lp, times.eval = NULL, centered = FALSE)
time |
Survival time |
event |
Status indicator |
lp |
Linear predictors |
times.eval |
Survival time to evaluate |
centered |
Should we center the survival curve?
See |
list containing cumulative base hazard
NULLNULL
Derived from c060::predictProb.coxnet
glmnet_survcurve(object, time, event, x, survtime)glmnet_survcurve(object, time, event, x, survtime)
object |
|
time |
Survival time |
event |
Status indicator |
x |
Predictor matrix |
survtime |
Survival time to evaluate |
list containing predicted survival probabilities and linear predictors for all samples
NULLNULL
Extract the names and type of selected variables from fitted high-dimensional Cox models.
infer_variable_type(object, x)infer_variable_type(object, x)
object |
Model object. |
x |
Data matrix used to fit the model. |
A list containing the index, name, type and range of the selected variables.
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) infer_variable_type(fit, x)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) infer_variable_type(fit, x)
Kaplan-Meier plot with number at risk table for internal calibration and external calibration results
kmplot( object, group.name = NULL, time.at = NULL, col.pal = c("JCO", "Lancet", "NPG", "AAAS") )kmplot( object, group.name = NULL, time.at = NULL, col.pal = c("JCO", "Lancet", "NPG", "AAAS") )
object |
An object returned by |
group.name |
Risk group labels. Default is Group 1, Group 2, ..., Group k. |
time.at |
Time points to evaluate the number at risk. |
col.pal |
Color palette to use. Possible values are
|
data("smart") # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso(x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11) # Internal calibration cal.int <- calibrate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "cv", nfolds = 5, pred.at = 365 * 9, ngroup = 3 ) kmplot( cal.int, group.name = c("High risk", "Medium risk", "Low risk"), time.at = 1:6 * 365 ) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 3 ) kmplot( cal.ext, group.name = c("High risk", "Medium risk", "Low risk"), time.at = 1:6 * 365 )data("smart") # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso(x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11) # Internal calibration cal.int <- calibrate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "cv", nfolds = 5, pred.at = 365 * 9, ngroup = 3 ) kmplot( cal.int, group.name = c("High risk", "Medium risk", "Low risk"), time.at = 1:6 * 365 ) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 3 ) kmplot( cal.ext, group.name = c("High risk", "Medium risk", "Low risk"), time.at = 1:6 * 365 )
Log-rank test for internal calibration and external calibration results
logrank_test(object)logrank_test(object)
object |
An object returned by |
data("smart") # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # Internal calibration cal.int <- calibrate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "cv", nfolds = 5, pred.at = 365 * 9, ngroup = 3 ) logrank_test(cal.int) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 3 ) logrank_test(cal.ext)data("smart") # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # Internal calibration cal.int <- calibrate( x, time, event, model.type = "lasso", alpha = 1, lambda = fit$lambda, method = "cv", nfolds = 5, pred.at = 365 * 9, ngroup = 3 ) logrank_test(cal.int) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 3 ) logrank_test(cal.ext)
Derived from peperr:::basesurv and gbm::basehaz.gbm.
ncvreg_basesurv(time, event, lp, times.eval = NULL, centered = FALSE)ncvreg_basesurv(time, event, lp, times.eval = NULL, centered = FALSE)
time |
Survival time |
event |
Status indicator |
lp |
Linear predictors |
times.eval |
Survival time to evaluate |
centered |
Should we center the survival curve?
See |
list containing cumulative base hazard
NULLNULL
Derived from c060::predictProb.coxnet
ncvreg_survcurve(object, time, event, x, survtime)ncvreg_survcurve(object, time, event, x, survtime)
object |
|
time |
Survival time |
event |
Status indicator |
x |
Predictor matrix |
survtime |
Survival time to evaluate |
list containing predicted survival probabilities and linear predictors for all samples
NULLNULL
Derived from peperr:::basesurv and gbm::basehaz.gbm.
penalized_basesurv(time, event, lp, times.eval = NULL, centered = FALSE)penalized_basesurv(time, event, lp, times.eval = NULL, centered = FALSE)
time |
Survival time |
event |
Status indicator |
lp |
Linear predictors |
times.eval |
Survival time to evaluate |
centered |
Should we center the survival curve?
See |
list containing cumulative base hazard
NULLNULL
Derived from c060::predictProb.coxnet
penalized_survcurve(object, time, event, x, survtime)penalized_survcurve(object, time, event, x, survtime)
object |
|
time |
Survival time |
event |
Status indicator |
x |
Predictor matrix |
survtime |
Survival time to evaluate |
list containing predicted survival probabilities and linear predictors for all samples
NULLNULL
Plot calibration results
## S3 method for class 'hdnom.calibrate' plot( x, xlim = c(0, 1), ylim = c(0, 1), col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ... )## S3 method for class 'hdnom.calibrate' plot( x, xlim = c(0, 1), ylim = c(0, 1), col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ... )
x |
An object returned by |
xlim |
x axis limits of the plot. |
ylim |
y axis limits of the plot. |
col.pal |
Color palette to use. Possible values are
|
... |
Other parameters for |
NULLNULL
Plot external calibration results
## S3 method for class 'hdnom.calibrate.external' plot( x, xlim = c(0, 1), ylim = c(0, 1), col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ... )## S3 method for class 'hdnom.calibrate.external' plot( x, xlim = c(0, 1), ylim = c(0, 1), col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ... )
x |
An object returned by |
xlim |
x axis limits of the plot. |
ylim |
y axis limits of the plot. |
col.pal |
Color palette to use. Possible values are
|
... |
Other parameters for |
NULLNULL
Plot model comparison by calibration results
## S3 method for class 'hdnom.compare.calibrate' plot( x, xlim = c(0, 1), ylim = c(0, 1), col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ... )## S3 method for class 'hdnom.compare.calibrate' plot( x, xlim = c(0, 1), ylim = c(0, 1), col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ... )
x |
An object returned by |
xlim |
x axis limits of the plot. |
ylim |
y axis limits of the plot. |
col.pal |
Color palette to use. Possible values are
|
... |
Other parameters (not used). |
NULLNULL
Plot model comparison by validation results
## S3 method for class 'hdnom.compare.validate' plot( x, interval = FALSE, col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ylim = NULL, ... )## S3 method for class 'hdnom.compare.validate' plot( x, interval = FALSE, col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ylim = NULL, ... )
x |
An object returned by |
interval |
Show maximum, minimum, 0.25, and 0.75 quantiles of
time-dependent AUC as ribbons? Default is |
col.pal |
Color palette to use. Possible values are
|
ylim |
Range of y coordinates. For example, |
... |
Other parameters (not used). |
NULLNULL
Plot nomogram objects
## S3 method for class 'hdnom.nomogram' plot(x, ...)## S3 method for class 'hdnom.nomogram' plot(x, ...)
x |
An object returned by |
... |
Other parameters. |
NULLNULL
Plot optimism-corrected time-dependent discrimination curves for validation
## S3 method for class 'hdnom.validate' plot(x, col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ylim = NULL, ...)## S3 method for class 'hdnom.validate' plot(x, col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ylim = NULL, ...)
x |
An object returned by |
col.pal |
Color palette to use. Possible values are
|
ylim |
Range of y coordinates. For example, |
... |
Other parameters (not used). |
NULLNULL
Plot time-dependent discrimination curves for external validation
## S3 method for class 'hdnom.validate.external' plot(x, col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ylim = NULL, ...)## S3 method for class 'hdnom.validate.external' plot(x, col.pal = c("JCO", "Lancet", "NPG", "AAAS"), ylim = NULL, ...)
x |
An object returned by |
col.pal |
Color palette to use. Possible values are
|
ylim |
Range of y coordinates. For example, |
... |
Other parameters (not used). |
NULLNULL
Predict overall survival probability at certain time points from fitted Cox models.
## S3 method for class 'hdnom.model' predict(object, x, y, newx, pred.at, ...)## S3 method for class 'hdnom.model' predict(object, x, y, newx, pred.at, ...)
object |
Model object. |
x |
Data matrix used to fit the model. |
y |
Response matrix made with |
newx |
Matrix (with named columns) of new values for |
pred.at |
Time point at which prediction should take place. |
... |
Other parameters (not used). |
A nrow(newx) x length(pred.at) matrix containing
overall survival probablity.
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) predict(fit, x, y, newx = x[101:105, ], pred.at = 1:10 * 365)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) predict(fit, x, y, newx = x[101:105, ], pred.at = 1:10 * 365)
Print calibration results
## S3 method for class 'hdnom.calibrate' print(x, ...)## S3 method for class 'hdnom.calibrate' print(x, ...)
x |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Print external calibration results
## S3 method for class 'hdnom.calibrate.external' print(x, ...)## S3 method for class 'hdnom.calibrate.external' print(x, ...)
x |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Print model comparison by calibration results
## S3 method for class 'hdnom.compare.calibrate' print(x, ...)## S3 method for class 'hdnom.compare.calibrate' print(x, ...)
x |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Print model comparison by validation results
## S3 method for class 'hdnom.compare.validate' print(x, ...)## S3 method for class 'hdnom.compare.validate' print(x, ...)
x |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Print high-dimensional Cox model objects
## S3 method for class 'hdnom.model' print(x, ...)## S3 method for class 'hdnom.model' print(x, ...)
x |
Model object. |
... |
Other parameters (not used). |
data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) print(fit)data("smart") x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT y <- survival::Surv(time, event) fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11) print(fit)
Print nomograms objects
## S3 method for class 'hdnom.nomogram' print(x, ...)## S3 method for class 'hdnom.nomogram' print(x, ...)
x |
An object returned by |
... |
Other parameters. |
NULLNULL
Print validation results
## S3 method for class 'hdnom.validate' print(x, ...)## S3 method for class 'hdnom.validate' print(x, ...)
x |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Print external validation results
## S3 method for class 'hdnom.validate.external' print(x, ...)## S3 method for class 'hdnom.validate.external' print(x, ...)
x |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Imputed SMART study data (no missing values).
data(smart)data(smart)
A numeric matrix with 3873 samples, and 29 variables (27 variables + time variable + event variable):
Demographics
SEX - gender
AGE - age in years
Classical risk factors
SMOKING - smoking (never, former, current)
PACKYRS - in years
ALCOHOL - alcohol use (never, former, current)
BMI - Body mass index, in kg/m^2
DIABETES
Blood pressure
SYSTH - Systolic, by hand, in mm Hg
SYSTBP - Systolic, automatic, in mm Hg
DIASTH - Diastolic, by hand, in mm Hg
DIASTBP - Diastolic, automatic, in mm Hg
Lipid levels
CHOL - Total cholesterol, in mmol/L
HDL - High-density lipoprotein cholesterol, in mmol/L
LDL - Low-density lipoprotein cholesterol, in mmol/L
TRIG - Triglycerides, in mmol/L
Previous symptomatic atherosclerosis
CEREBRAL - Cerebral
CARDIAC - Coronary
PERIPH - Peripheral
AAA - Abdominal aortic aneurysm
Markers of atherosclerosis
HOMOC - Homocysteine, in mol/L
GLUT - Glutamine, in mol/L
CREAT - Creatinine clearance, in mL/min
ALBUMIN - Albumin (no, micro, macro)
IMT - Intima media thickness, in mm
STENOSIS - Carotid artery stenosis > 50%
See data-raw/smart.R for the code to generate this data.
Steyerberg, E. W. (2008). Clinical prediction models: a practical approach to development, validation, and updating. Springer Science & Business Media.
data(smart) dim(smart)data(smart) dim(smart)
Original SMART study data (with missing values) from Steyerberg et, al. 2008.
data(smarto)data(smarto)
A numeric matrix with 3873 samples, and 29 variables (27 variables + time variable + event variable):
Demographics
SEX - gender
AGE - age in years
Classical risk factors
SMOKING - smoking (never, former, current)
PACKYRS - in years
ALCOHOL - alcohol use (never, former, current)
BMI - Body mass index, in kg/m^2
DIABETES
Blood pressure
SYSTH - Systolic, by hand, in mm Hg
SYSTBP - Systolic, automatic, in mm Hg
DIASTH - Diastolic, by hand, in mm Hg
DIASTBP - Diastolic, automatic, in mm Hg
Lipid levels
CHOL - Total cholesterol, in mmol/L
HDL - High-density lipoprotein cholesterol, in mmol/L
LDL - Low-density lipoprotein cholesterol, in mmol/L
TRIG - Triglycerides, in mmol/L
Previous symptomatic atherosclerosis
CEREBRAL - Cerebral
CARDIAC - Coronary
PERIPH - Peripheral
AAA - Abdominal aortic aneurysm
Markers of atherosclerosis
HOMOC - Homocysteine, in mol/L
GLUT - Glutamine, in mol/L
CREAT - Creatinine clearance, in mL/min
ALBUMIN - Albumin (no, micro, macro)
IMT - Intima media thickness, in mm
STENOSIS - Carotid artery stenosis > 50%
Steyerberg, E. W. (2008). Clinical prediction models: a practical approach to development, validation, and updating. Springer Science & Business Media.
data(smarto) dim(smarto)data(smarto) dim(smarto)
Summary of calibration results
## S3 method for class 'hdnom.calibrate' summary(object, ...)## S3 method for class 'hdnom.calibrate' summary(object, ...)
object |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Summary of external calibration results
## S3 method for class 'hdnom.calibrate.external' summary(object, ...)## S3 method for class 'hdnom.calibrate.external' summary(object, ...)
object |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Summary of model comparison by calibration results
## S3 method for class 'hdnom.compare.calibrate' summary(object, ...)## S3 method for class 'hdnom.compare.calibrate' summary(object, ...)
object |
An object returned by |
... |
Other parameters (not used). |
NULLNULL
Summary of model comparison by validation results
## S3 method for class 'hdnom.compare.validate' summary(object, silent = FALSE, ...)## S3 method for class 'hdnom.compare.validate' summary(object, silent = FALSE, ...)
object |
An object |
silent |
Print summary table header or not,
default is |
... |
Other parameters (not used). |
NULLNULL
Summary of validation results
## S3 method for class 'hdnom.validate' summary(object, silent = FALSE, ...)## S3 method for class 'hdnom.validate' summary(object, silent = FALSE, ...)
object |
A |
silent |
Print summary table header or not,
default is |
... |
Other parameters (not used). |
NULLNULL
Summary of external validation results
## S3 method for class 'hdnom.validate.external' summary(object, silent = FALSE, ...)## S3 method for class 'hdnom.validate.external' summary(object, silent = FALSE, ...)
object |
An object returned by |
silent |
Print summary table header or not,
default is |
... |
Other parameters (not used). |
NULLNULL
Plot theme (ggplot2) for hdnom
theme_hdnom(base_size = 14)theme_hdnom(base_size = 14)
base_size |
base font size |
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 )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$EVENT[1:500] # y = survival::Surv(time, event) # # set.seed(1010) # val.boot = validate( # x, time, event, model.type = "flasso", # lambda1 = 5, lambda2 = 2, # method = "bootstrap", boot.times = 10, # tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, # seed = 1010) # # val.cv = validate( # x, time, event, model.type = "scad", # gamma = 3.7, alpha = 1, lambda = 0.05, # method = "cv", nfolds = 5, # tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, # seed = 1010) # # val.repcv = validate( # x, time, event, model.type = "mnet", # gamma = 3, alpha = 0.3, lambda = 0.05, # method = "repeated.cv", nfolds = 5, rep.times = 3, # tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365, # seed = 1010) # # print(val.boot) # summary(val.boot) # plot(val.boot) # # print(val.cv) # summary(val.cv) # plot(val.cv) # # print(val.repcv) # summary(val.repcv) # plot(val.repcv)data(smart) x <- as.matrix(smart[, -c(1, 2)])[1:500, ] time <- smart$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)
Externally validate high-dimensional Cox models with time-dependent AUC
validate_external( object, x, time, event, x_new, time_new, event_new, tauc.type = c("CD", "SZ", "UNO"), tauc.time )validate_external( object, x, time, event, x_new, time_new, event_new, tauc.type = c("CD", "SZ", "UNO"), tauc.time )
object |
Model object fitted by |
x |
Matrix of training data used for fitting the model. |
time |
Survival time of the training data.
Must be of the same length with the number of rows as |
event |
Status indicator of the training data,
normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |
x_new |
Matrix of predictors for the external validation data. |
time_new |
Survival time of the external validation data.
Must be of the same length with the number of rows as |
event_new |
Status indicator of the external validation data,
normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |
tauc.type |
Type of time-dependent AUC.
Including |
tauc.time |
Numeric vector. Time points at which to evaluate the time-dependent AUC. |
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) # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external validation data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # External validation with time-dependent AUC val.ext <- validate_external( fit, x, time, event, x_new, time_new, event_new, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365 ) print(val.ext) summary(val.ext) plot(val.ext) # # Test fused lasso, MCP, and Snet models # data(smart) # # Use first 600 samples as training data # # (the data used for internal validation) # x <- as.matrix(smart[, -c(1, 2)])[1:600, ] # time <- smart$TEVENT[1:600] # event <- smart$EVENT[1:600] # # # Take 500 samples as external validation data. # # In practice, usually use data collected in other studies. # x_new <- as.matrix(smart[, -c(1, 2)])[1001:1500, ] # time_new <- smart$TEVENT[1001:1500] # event_new <- smart$EVENT[1001:1500] # # flassofit <- fit_flasso(x, survival::Surv(time, event), nfolds = 5, seed = 11) # scadfit <- fit_mcp(x, survival::Surv(time, event), nfolds = 5, seed = 11) # mnetfit <- fit_snet(x, survival::Surv(time, event), nfolds = 5, seed = 11) # # val.ext1 <- validate_external( # flassofit, x, time, event, # x_new, time_new, event_new, # tauc.type = "UNO", # tauc.time = seq(0.25, 2, 0.25) * 365) # # val.ext2 <- validate_external( # scadfit, x, time, event, # x_new, time_new, event_new, # tauc.type = "CD", # tauc.time = seq(0.25, 2, 0.25) * 365) # # val.ext3 <- validate_external( # mnetfit, x, time, event, # x_new, time_new, event_new, # tauc.type = "SZ", # tauc.time = seq(0.25, 2, 0.25) * 365) # # print(val.ext1) # summary(val.ext1) # plot(val.ext1) # # print(val.ext2) # summary(val.ext2) # plot(val.ext2) # # print(val.ext3) # summary(val.ext3) # plot(val.ext3)data(smart) # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external validation data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # External validation with time-dependent AUC val.ext <- validate_external( fit, x, time, event, x_new, time_new, event_new, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365 ) print(val.ext) summary(val.ext) plot(val.ext) # # Test fused lasso, MCP, and Snet models # data(smart) # # Use first 600 samples as training data # # (the data used for internal validation) # x <- as.matrix(smart[, -c(1, 2)])[1:600, ] # time <- smart$TEVENT[1:600] # event <- smart$EVENT[1:600] # # # Take 500 samples as external validation data. # # In practice, usually use data collected in other studies. # x_new <- as.matrix(smart[, -c(1, 2)])[1001:1500, ] # time_new <- smart$TEVENT[1001:1500] # event_new <- smart$EVENT[1001:1500] # # flassofit <- fit_flasso(x, survival::Surv(time, event), nfolds = 5, seed = 11) # scadfit <- fit_mcp(x, survival::Surv(time, event), nfolds = 5, seed = 11) # mnetfit <- fit_snet(x, survival::Surv(time, event), nfolds = 5, seed = 11) # # val.ext1 <- validate_external( # flassofit, x, time, event, # x_new, time_new, event_new, # tauc.type = "UNO", # tauc.time = seq(0.25, 2, 0.25) * 365) # # val.ext2 <- validate_external( # scadfit, x, time, event, # x_new, time_new, event_new, # tauc.type = "CD", # tauc.time = seq(0.25, 2, 0.25) * 365) # # val.ext3 <- validate_external( # mnetfit, x, time, event, # x_new, time_new, event_new, # tauc.type = "SZ", # tauc.time = seq(0.25, 2, 0.25) * 365) # # print(val.ext1) # summary(val.ext1) # plot(val.ext1) # # print(val.ext2) # summary(val.ext2) # plot(val.ext2) # # print(val.ext3) # summary(val.ext3) # plot(val.ext3)