## ---- include = FALSE---------------------------------------------------------
dpi = 125
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
dpi=dpi,
fig.retina=1,
fig.width=1440/dpi, #4:3 FHD
fig.height=1080/dpi,
out.width="100%",
crop = NULL,
warning = T,
error = T
)
rm(dpi)
## ---- eval = FALSE------------------------------------------------------------
# install.packages("Coxmos")
## ---- eval = FALSE------------------------------------------------------------
# install.packages("devtools")
# devtools::install_github("BiostatOmics/Coxmos", build_vignettes = TRUE)
## ----setup, results = "hide"--------------------------------------------------
# load Coxmos
library(Coxmos)
## ---- eval=FALSE--------------------------------------------------------------
# # install.packages("RColorConesa")
# library(RColorConesa)
## ----load data----------------------------------------------------------------
# load dataset
data("X_proteomic")
data("Y_proteomic")
X <- X_proteomic
Y <- Y_proteomic
rm(X_proteomic, Y_proteomic)
## ----data dimensions, echo = FALSE--------------------------------------------
knitr::kable(X[1:5,1:5])
knitr::kable(Y[1:5,])
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(dim(X), col.names = "X")
knitr::kable(dim(Y), col.names = "Y")
## -----------------------------------------------------------------------------
ggp_density.event <- plot_events(Y = Y,
categories = c("Censored","Death"), #name for FALSE/0 (Censored) and TRUE/1 (Event)
y.text = "Number of observations",
roundTo = 0.5,
max.breaks = 15)
## ----fig.small = T------------------------------------------------------------
ggp_density.event$plot
## -----------------------------------------------------------------------------
set.seed(123)
index_train <- caret::createDataPartition(Y$event,
p = .7, # 70% train
list = FALSE,
times = 1)
## -----------------------------------------------------------------------------
X_train <- X[index_train,] #106x369
Y_train <- Y[index_train,]
X_test <- X[-index_train,] #44x369
Y_test <- Y[-index_train,]
## ---- eval=FALSE, message=T---------------------------------------------------
# # classical approach
# cox_model <- cox(X = X_train, Y = Y_train,
# x.center = T, x.scale = F,
# remove_near_zero_variance = T, remove_zero_variance = T, toKeep.zv = NULL,
# remove_non_significant = F, alpha = 0.05,
# MIN_EPV = 5, FORCE = F, returnData = T, verbose = F)
## -----------------------------------------------------------------------------
EPV <- getEPV(X_train, Y_train)
## -----------------------------------------------------------------------------
EPV
## ---- warning=F, eval=FALSE---------------------------------------------------
# # run cv.coxEN
# cv.coxen_res <- cv.coxEN(X = X_train, Y = Y_train,
# EN.alpha.list = c(0.1, 0.5, 0.9),
# max.variables = ncol(X_train),
# n_run = 2, k_folds = 5,
# x.center = T, x.scale = F,
# remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
# remove_variance_at_fold_level = F,
# remove_non_significant = F, alpha = 0.05,
# w_AIC = 0, w_C.Index = 0, w_AUC = 1, w_I.BRIER = 0, times = NULL, max_time_points = 15,
# MIN_AUC_INCREASE = 0.01, MIN_AUC = 0.8, MIN_COMP_TO_CHECK = 3,
# pred.attr = "mean", pred.method = "cenROC", fast_mode = F,
# MIN_EPV = 5, return_models = F,
# returnData = F,
# PARALLEL = F, verbose = F, seed = 123)
## ---- eval=FALSE--------------------------------------------------------------
# cv.coxen_res
## ---- warning=F---------------------------------------------------------------
coxen_model <- coxEN(X = X_train, Y = Y_train,
EN.alpha = 0.5, #cv.coxen_res$opt.EN.alpha,
max.variables = 8, #cv.coxen_res$opt.nvar,
x.center = T, x.scale = F,
remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
remove_non_significant = F, alpha = 0.05,
MIN_EPV = 5, returnData = T, verbose = F)
## -----------------------------------------------------------------------------
coxen_model
## ---- warning=F---------------------------------------------------------------
coxen_model <- coxEN(X = X_train, Y = Y_train,
EN.alpha = 0.5, #cv.coxen_res$opt.EN.alpha
max.variables = 8, #cv.coxen_res$opt.nvar
x.center = T, x.scale = F,
remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
remove_non_significant = T, alpha = 0.05,
MIN_EPV = 5, returnData = T, verbose = F)
## -----------------------------------------------------------------------------
coxen_model
## -----------------------------------------------------------------------------
coxen_model$nsv
## ---- warning=F, eval=FALSE---------------------------------------------------
# # run cv.plsicox
# cv.splsicox_res <- cv.splsicox(X = X_train, Y = Y_train,
# max.ncomp = 2, penalty.list = c(0.5, 0.9),
# n_run = 2, k_folds = 5,
# x.center = T, x.scale = F,
# remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
# remove_variance_at_fold_level = F,
# remove_non_significant_models = F, alpha = 0.05,
# w_AIC = 0, w_C.Index = 0, w_AUC = 1, w_I.BRIER = 0, times = NULL, max_time_points = 15,
# MIN_AUC_INCREASE = 0.01, MIN_AUC = 0.8, MIN_COMP_TO_CHECK = 3,
# pred.attr = "mean", pred.method = "cenROC", fast_mode = F,
# MIN_EPV = 5, return_models = F, remove_non_significant = F, returnData = F,
# PARALLEL = F, verbose = F, seed = 123)
## ---- eval=FALSE--------------------------------------------------------------
# cv.splsicox_res
## ---- fig.small=T, warning=F, eval=FALSE--------------------------------------
# # plot cv.plsicox
# cv.splsicox_res$plot_AUC
## -----------------------------------------------------------------------------
splsicox_model <- splsicox(X = X_train, Y = Y_train,
n.comp = 1, #cv.splsicox_res$opt.comp,
penalty = 0.9, #cv.splsicox_res$opt.spv_penalty,
x.center = T, x.scale = F,
remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
remove_non_significant = T,
MIN_EPV = 5, returnData = T, verbose = F)
splsicox_model
## ---- warning==FALSE, eval=FALSE----------------------------------------------
# # run cv.splsdrcox
# cv.splsdrcox_penalty_res <- cv.splsdrcox_penalty(X = X_train, Y = Y_train,
# max.ncomp = 2, penalty.list = c(0.5, 0.9),
# n_run = 2, k_folds = 5,
# x.center = T, x.scale = F,
# remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
# remove_non_significant_models = F, alpha = 0.05,
# w_AIC = 0, w_C.Index = 0, w_AUC = 1, w_I.BRIER = 0, times = NULL,
# MIN_AUC_INCREASE = 0.01, MIN_AUC = 0.8, MIN_COMP_TO_CHECK = 3,
# pred.attr = "mean", pred.method = "cenROC", fast_mode = F,
# MIN_EPV = 5, return_models = F,
# PARALLEL = F, verbose = F, seed = 123)
## ---- eval=FALSE--------------------------------------------------------------
# cv.splsdrcox_penalty_res
## ---- fig.small=T, warning=F, eval=FALSE--------------------------------------
# # plot cv.plsicox
# cv.splsdrcox_penalty_res$plot_AUC
## -----------------------------------------------------------------------------
splsdrcox_penalty_model <- splsdrcox_penalty(X = X_train, Y = Y_train,
n.comp = 2, #cv.splsdrcox_penalty_res$opt.comp,
penalty = 0.5, #cv.splsdrcox_penalty_res$opt.eta,
x.center = T, x.scale = F,
remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
remove_non_significant = T,
MIN_EPV = 5, returnData = T, verbose = F)
splsdrcox_penalty_model
## ---- warning=FALSE, eval=FALSE-----------------------------------------------
# # run cv.splsdrcox
# cv.splsdrcox_dynamic_res <- cv.splsdrcox(X = X_train, Y = Y_train,
# max.ncomp = 2, vector = NULL,
# MIN_NVAR = 10, MAX_NVAR = 400,
# n.cut_points = 10, EVAL_METHOD = "AUC",
# n_run = 2, k_folds = 5,
# x.center = T, x.scale = F,
# remove_near_zero_variance = T, remove_zero_variance = F,
# toKeep.zv = NULL,
# remove_non_significant_models = F, alpha = 0.05,
# remove_variance_at_fold_level = F, remove_non_significant = F,
# w_AIC = 0, w_C.Index = 0, w_AUC = 1, w_I.BRIER = 0,
# times = NULL, max_time_points = 15, returnData = F,
# MIN_AUC_INCREASE = 0.01, MIN_AUC = 0.8, MIN_COMP_TO_CHECK = 3,
# pred.attr = "mean", pred.method = "cenROC", fast_mode = F,
# MIN_EPV = 5, return_models = F,
# PARALLEL = F, verbose = F, seed = 123)
## ---- eval=FALSE--------------------------------------------------------------
# cv.splsdrcox_dynamic_res
## -----------------------------------------------------------------------------
splsdrcox_dynamic_model <- splsdrcox(X = X_train, Y = Y_train,
n.comp = 2, #cv.splsdrcox_dynamic_res$opt.comp,
vector = 369, #cv.splsdrcox_dynamic_res$opt.nvar,
x.center = T, x.scale = F,
remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
MIN_NVAR = 10, MAX_NVAR = NULL, n.cut_points = 5,
MIN_AUC_INCREASE = 0.01,
EVAL_METHOD = "AUC", pred.method = "cenROC", max.iter = 200,
remove_non_significant = T,
MIN_EPV = 5, returnData = T, verbose = F)
splsdrcox_dynamic_model
## ---- warning=FALSE, eval=FALSE-----------------------------------------------
# # run cv.splsdrcox
# cv.splsdacox_dynamic_res <- cv.splsdacox(X = X_train, Y = Y_train,
# max.ncomp = 2, vector = NULL,
# MIN_NVAR = 10, MAX_NVAR = 400,
# n.cut_points = 10, EVAL_METHOD = "AUC",
# n_run = 2, k_folds = 5,
# x.center = T, x.scale = F,
# remove_near_zero_variance = T, remove_zero_variance = F,
# toKeep.zv = NULL,
# remove_variance_at_fold_level = F, remove_non_significant = F,
# remove_non_significant_models = F, alpha = 0.05,
# w_AIC = 0, w_C.Index = 0, w_AUC = 1, w_I.BRIER = 0,
# times = NULL, max_time_points = 15, returnData = F,
# MIN_AUC_INCREASE = 0.01, MIN_AUC = 0.8, MIN_COMP_TO_CHECK = 3,
# pred.attr = "mean", pred.method = "cenROC", fast_mode = F,
# MIN_EPV = 5, return_models = F, max.iter = 200,
# PARALLEL = F, verbose = F, seed = 123)
## ---- eval=FALSE, eval=FALSE--------------------------------------------------
# cv.splsdacox_dynamic_res
## -----------------------------------------------------------------------------
splsdacox_dynamic_model <- splsdacox(X = X_train, Y = Y_train,
n.comp = 2, #cv.splsdacox_dynamic_res$opt.comp,
vector = 330, #cv.splsdacox_dynamic_res$opt.nvar,
x.center = T, x.scale = F,
remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
MIN_NVAR = 10, MAX_NVAR = NULL, n.cut_points = 5,
MIN_AUC_INCREASE = 0.01,
EVAL_METHOD = "AUC", pred.method = "cenROC", max.iter = 200,
remove_non_significant = T,
MIN_EPV = 5, returnData = T, verbose = F)
splsdacox_dynamic_model
## -----------------------------------------------------------------------------
lst_models <- list("COX-EN" = coxen_model,
"sPLS-ICOX" = splsicox_model,
"sPLS-DRCOX-Penalty" = splsdrcox_penalty_model,
"sPLS-DRCOX-Dynamic" = splsdrcox_dynamic_model,
"sPLS-DACOX-Dynamic" = splsdacox_dynamic_model)
eval_results <- eval_Coxmos_models(lst_models = lst_models,
X_test = X_test, Y_test = Y_test,
pred.method = "cenROC",
pred.attr = "mean",
times = NULL, max_time_points = 15,
PARALLEL = F)
## ---- eval=FALSE--------------------------------------------------------------
# lst_evaluators <- c(cenROC = "cenROC", risksetROC = "risksetROC")
#
# eval_results <- purrr::map(lst_evaluators, ~eval_Coxmos_models(lst_models = lst_models,
# X_test = X_test, Y_test = Y_test,
# pred.method = .,
# pred.attr = "mean",
# times = NULL,
# max_time_points = 15,
# PARALLEL = F))
## -----------------------------------------------------------------------------
eval_results
## ---- warning=F---------------------------------------------------------------
lst_eval_results <- plot_evaluation(eval_results, evaluation = "AUC", pred.attr = "mean")
lst_eval_results_BRIER <- plot_evaluation(eval_results, evaluation = "IBS", pred.attr = "mean")
## ---- fig.small=T, warning=F--------------------------------------------------
lst_eval_results$lineplot.mean
lst_eval_results$lst_plot_comparisons$anova
# lst_eval_results$cenROC$lst_plots$lineplot.mean
# lst_eval_results$cenROC$lst_plot_comparisons$t.test
## -----------------------------------------------------------------------------
lst_models_time <- list(#cv.coxen_res,
coxen_model,
#cv.splsicox_res,
splsicox_model,
#cv.splsdrcox_penalty_res,
splsdrcox_penalty_model,
#cv.splsdrcox_dynamic_res,
splsdrcox_dynamic_model,
#cv.splsdacox_dynamic_res,
splsdacox_dynamic_model,
eval_results)
## -----------------------------------------------------------------------------
ggp_time <- plot_time.list(lst_models_time)
## ---- fig.small=T, warning=F--------------------------------------------------
ggp_time
## -----------------------------------------------------------------------------
#lst_forest_plot <- plot_forest.list(lst_models)
lst_forest_plot <- plot_forest(lst_models$`sPLS-DRCOX-Dynamic`)
## ---- fig.small=T, warning=F--------------------------------------------------
#lst_forest_plot$`sPLS-DRCOX`
lst_forest_plot
## -----------------------------------------------------------------------------
#lst_ph_ggplot <- plot_proportionalHazard.list(lst_models)
lst_ph_ggplot <- plot_proportionalHazard(lst_models$`sPLS-DRCOX-Dynamic`)
## ---- fig.small=T, warning=F--------------------------------------------------
#lst_ph_ggplot$`sPLS-DRCOX`
lst_ph_ggplot
## -----------------------------------------------------------------------------
#density.plots.lp <- plot_cox.event.list(lst_models, type = "lp")
density.plots.lp <- plot_cox.event(lst_models$`sPLS-DRCOX-Dynamic`, type = "lp")
## ---- fig.small=T, warning=F--------------------------------------------------
# density.plots.lp$`sPLS-DRCOX`$plot.density
# density.plots.lp$`sPLS-DRCOX`$plot.histogram
density.plots.lp$plot.density
density.plots.lp$plot.histogram
## -----------------------------------------------------------------------------
ggp_scores <- plot_PLS_Coxmos(model = lst_models$`sPLS-DRCOX-Dynamic`,
comp = c(1,2), mode = "scores")
## ---- fig.small=T, warning=F--------------------------------------------------
ggp_scores$plot
## -----------------------------------------------------------------------------
ggp_loadings <- plot_PLS_Coxmos(model = lst_models$`sPLS-DRCOX-Dynamic`,
comp = c(1,2), mode = "loadings",
top = 10) #length from 0,0
## ---- fig.small=T, warning=F--------------------------------------------------
ggp_loadings$plot
## -----------------------------------------------------------------------------
ggp_biplot <- plot_PLS_Coxmos(model = lst_models$`sPLS-DRCOX-Dynamic`,
comp = c(1,2), mode = "biplot",
top = 15,
only_top = T,
overlaps = 20)
## ---- fig.small=T, warning=F--------------------------------------------------
ggp_biplot$plot
## ---- warning=F---------------------------------------------------------------
variable_auc_results <- eval_Coxmos_model_per_variable(model = lst_models$`sPLS-DRCOX-Dynamic`,
X_test = lst_models$`sPLS-DRCOX-Dynamic`$X_input,
Y_test = lst_models$`sPLS-DRCOX-Dynamic`$Y_input,
pred.method = "cenROC", pred.attr = "mean",
times = NULL, max_time_points = 15,
PARALLEL = FALSE)
variable_auc_plot_train <- plot_evaluation(variable_auc_results, evaluation = "AUC")
## ---- fig.small=T, warning=F--------------------------------------------------
variable_auc_plot_train$lst_plots$lineplot.mean
## -----------------------------------------------------------------------------
# ggp.simulated_beta <- plot_pseudobeta.list(lst_models = lst_models,
# error.bar = T, onlySig = T, alpha = 0.05,
# zero.rm = T, auto.limits = T, top = 20,
# show_percentage = T, size_percentage = 2, verbose = F)
ggp.simulated_beta <- plot_pseudobeta(model = lst_models$`sPLS-DRCOX-Dynamic`,
error.bar = T, onlySig = T, alpha = 0.05,
zero.rm = T, auto.limits = T, top = 20,
show_percentage = T, size_percentage = 2)
## ---- fig.small=T, warning=F--------------------------------------------------
#ggp.simulated_beta$`sPLS-DRCOX`$plot
ggp.simulated_beta$plot
## -----------------------------------------------------------------------------
# LST_KM_RES_LP <- getAutoKM.list(type = "LP",
# lst_models = lst_models,
# comp = 1:10,
# top = 10,
# ori_data = T,
# BREAKTIME = NULL,
# only_sig = T, alpha = 0.05)
LST_KM_RES_LP <- getAutoKM(type = "LP",
model = lst_models$`sPLS-DRCOX-Dynamic`,
comp = 1:10,
top = 10,
ori_data = T,
BREAKTIME = NULL,
only_sig = T, alpha = 0.05)
## ---- fig.small=T, warning=F--------------------------------------------------
#LST_KM_RES_LP$`sPLS-DRCOX`$LST_PLOTS$LP
LST_KM_RES_LP$LST_PLOTS$LP
## -----------------------------------------------------------------------------
# lst_cutoff <- getCutoffAutoKM.list(LST_KM_RES_LP)
# LST_KM_TEST_LP <- getTestKM.list(lst_models = lst_models,
# X_test = X_test, Y_test = Y_test,
# type = "LP",
# BREAKTIME = NULL, n.breaks = 20,
# lst_cutoff = lst_cutoff)
lst_cutoff <- getCutoffAutoKM(LST_KM_RES_LP)
LST_KM_TEST_LP <- getTestKM(model = lst_models$`sPLS-DRCOX-Dynamic`,
X_test = X_test, Y_test = Y_test,
type = "LP",
BREAKTIME = NULL, n.breaks = 20,
cutoff = lst_cutoff)
## ---- fig.small=T, warning=F--------------------------------------------------
#LST_KM_TEST_LP$`sPLS-DRCOX`
LST_KM_TEST_LP
## -----------------------------------------------------------------------------
# LST_KM_RES_COMP <- getAutoKM.list(type = "COMP",
# lst_models = lst_models,
# comp = 1:10,
# top = 10,
# ori_data = T,
# BREAKTIME = NULL,
# n.breaks = 20,
# only_sig = T, alpha = 0.05)
LST_KM_RES_COMP <- getAutoKM(type = "COMP",
model = lst_models$`sPLS-DRCOX-Dynamic`,
comp = 1:10,
top = 10,
ori_data = T,
BREAKTIME = NULL,
n.breaks = 20,
only_sig = T, alpha = 0.05)
## ---- fig.small=T, warning=F--------------------------------------------------
# LST_KM_RES_COMP$`sPLS-DRCOX`$LST_PLOTS$comp_1
# LST_KM_RES_COMP$`sPLS-DRCOX`$LST_PLOTS$comp_2
LST_KM_RES_COMP$LST_PLOTS$comp_1
LST_KM_RES_COMP$LST_PLOTS$comp_2
## -----------------------------------------------------------------------------
# lst_cutoff <- getCutoffAutoKM.list(LST_KM_RES_COMP)
# LST_KM_TEST_COMP <- getTestKM.list(lst_models = lst_models,
# X_test = X_test, Y_test = Y_test,
# type = "COMP",
# BREAKTIME = NULL, n.breaks = 20,
# lst_cutoff = lst_cutoff)
lst_cutoff <- getCutoffAutoKM(LST_KM_RES_COMP)
LST_KM_TEST_COMP <- getTestKM(model = lst_models$`sPLS-DRCOX-Dynamic`,
X_test = X_test, Y_test = Y_test,
type = "COMP",
BREAKTIME = NULL, n.breaks = 20,
cutoff = lst_cutoff)
## ---- fig.small=T, warning=F--------------------------------------------------
# all patients could be categorize into the same group
# LST_KM_TEST_COMP$`sPLS-DRCOX`$comp_1
# LST_KM_TEST_COMP$`sPLS-DRCOX`$comp_2
LST_KM_TEST_COMP$comp_1
LST_KM_TEST_COMP$comp_2
## ---- warning=FALSE-----------------------------------------------------------
# LST_KM_RES_VAR <- getAutoKM.list(type = "VAR",
# lst_models = lst_models,
# comp = 1:10, #select how many components you want to compute for the pseudo beta
# top = 10,
# ori_data = T, #original data selected
# BREAKTIME = NULL,
# only_sig = T, alpha = 0.05)
LST_KM_RES_VAR <- getAutoKM(type = "VAR",
model = lst_models$`sPLS-DRCOX-Dynamic`,
comp = 1:10, #select how many components you want to compute for the pseudo beta
top = 10,
ori_data = T, #original data selected
BREAKTIME = NULL,
only_sig = T, alpha = 0.05)
## ---- fig.small=T, warning=F--------------------------------------------------
LST_KM_RES_VAR$LST_PLOTS$var_840
## -----------------------------------------------------------------------------
# lst_cutoff <- getCutoffAutoKM.list(LST_KM_RES_VAR)
# LST_KM_TEST_VAR <- getTestKM.list(lst_models = lst_models,
# X_test = X_test, Y_test = Y_test,
# type = "VAR", ori_data = T,
# BREAKTIME = NULL, n.breaks = 20,
# lst_cutoff = lst_cutoff)
lst_cutoff <- getCutoffAutoKM(LST_KM_RES_VAR)
LST_KM_TEST_VAR <- getTestKM(model = lst_models$`sPLS-DRCOX-Dynamic`,
X_test = X_test, Y_test = Y_test,
type = "VAR", ori_data = T,
BREAKTIME = NULL, n.breaks = 20,
cutoff = lst_cutoff)
## ---- fig.small=T, warning=F--------------------------------------------------
LST_KM_TEST_VAR$var_840
## -----------------------------------------------------------------------------
new_pat <- X_test[1,,drop=F]
## -----------------------------------------------------------------------------
knitr::kable(Y_test[rownames(new_pat),])
## ---- warning=F---------------------------------------------------------------
# ggp.simulated_beta_newObs <- plot_observation.pseudobeta.list(lst_models = lst_models,
# new_observation = new_pat,
# error.bar = T, onlySig = T, alpha = 0.05,
# zero.rm = T, auto.limits = T, show.betas = T, top = 20)
ggp.simulated_beta_newObs <- plot_observation.pseudobeta(model = lst_models$`sPLS-DRCOX-Dynamic`,
new_observation = new_pat,
error.bar = T, onlySig = T, alpha = 0.05,
zero.rm = T, auto.limits = T, show.betas = T, top = 20)
## ---- fig.small=T, warning=F--------------------------------------------------
#ggp.simulated_beta_newObs$`sPLS-DRCOX`$plot
ggp.simulated_beta_newObs$plot
## -----------------------------------------------------------------------------
pat_density <- plot_observation.eventDensity(observation = new_pat,
model = lst_models$`sPLS-DRCOX-Dynamic`,
time = NULL,
type = "lp")
## ---- fig.small=T, warning=F--------------------------------------------------
pat_density
## -----------------------------------------------------------------------------
pat_histogram <- plot_observation.eventHistogram(observation = new_pat,
model = lst_models$`sPLS-DRCOX-Dynamic`,
time = NULL,
type = "lp")
## ---- fig.small=T, warning=F--------------------------------------------------
pat_histogram
## -----------------------------------------------------------------------------
knitr::kable(Y_test[1:5,])
## ---- warning=F---------------------------------------------------------------
# lst_cox.comparison <- plot_LP.multipleObservations.list(lst_models = lst_models,
# new_observations = X_test[1:5,],
# error.bar = T, zero.rm = T, onlySig = T,
# alpha = 0.05, top = 10)
lst_cox.comparison <- plot_LP.multipleObservations(model = lst_models$`sPLS-DRCOX-Dynamic`,
new_observations = X_test[1:5,],
error.bar = T, zero.rm = T, onlySig = T,
alpha = 0.05, top = 10)
## ---- fig.small=T, warning=F--------------------------------------------------
# lst_cox.comparison$`sPLS-DRCOX-Dynamic`$plot
lst_cox.comparison$plot