## ---- include = FALSE---------------------------------------------------------
dpi = 125
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
dpi=dpi,
fig.retina=1,
fig.width=1440/dpi, #4:3 FHD
fig.height=1080/dpi,
out.width="100%",
crop = NULL,
warning = T,
error = T
)
rm(dpi)
## ---- eval = FALSE------------------------------------------------------------
# install.packages("Coxmos")
## ---- eval = FALSE------------------------------------------------------------
# install.packages("devtools")
# devtools::install_github("BiostatOmics/Coxmos", build_vignettes = TRUE)
## ----setup, results = "hide"--------------------------------------------------
# load Coxmos
library(Coxmos)
## ---- eval=FALSE--------------------------------------------------------------
# # install.packages("RColorConesa")
# library(RColorConesa)
## -----------------------------------------------------------------------------
# load dataset
data("X_multiomic", package = "Coxmos")
data("Y_multiomic", package = "Coxmos")
X <- X_multiomic
Y <- Y_multiomic
rm(X_multiomic, Y_multiomic)
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(X$mirna[1:5,1:5]);knitr::kable(X$proteomic[1:5,1:5])
knitr::kable(Y[1:5,])
## -----------------------------------------------------------------------------
ggp_density.event <- plot_events(Y = Y,
categories = c("Censored","Death"), #name for FALSE/0 (Censored) and TRUE/1 (Event)
y.text = "Number of observations",
roundTo = 0.5,
max.breaks = 15)
## ----fig.small = T------------------------------------------------------------
ggp_density.event$plot
## -----------------------------------------------------------------------------
set.seed(123)
index_train <- caret::createDataPartition(Y$event,
p = .7, #70 %
list = FALSE,
times = 1)
X_train <- list()
X_test <- list()
for(omic in names(X)){
X_train[[omic]] <- X[[omic]][index_train,,drop=F]
X_test[[omic]] <- X[[omic]][-index_train,,drop=F]
}
Y_train <- Y[index_train,]
Y_test <- Y[-index_train,]
## -----------------------------------------------------------------------------
EPV <- getEPV.mb(X_train, Y_train)
for(b in names(X_train)){
message(paste0("EPV = ", round(EPV[[b]], 4), ", for block ", b))
}
## ---- message=F---------------------------------------------------------------
x.center = c(mirna = T, proteomic = T) #if vector, must be named
x.scale = c(mirna = F, proteomic = F) #if vector, must be named
## ----warning=T, eval=F--------------------------------------------------------
# cv.sb.splsicox_res <- cv.sb.splsicox(X = X_train, Y = Y_train,
# max.ncomp = 2, penalty.list = c(0.5,0.9),
# n_run = 2, k_folds = 5,
# x.center = x.center, x.scale = x.scale,
# remove_near_zero_variance = T, remove_zero_variance = F, toKeep.zv = NULL,
# remove_variance_at_fold_level = F,
# remove_non_significant_models = F, alpha = 0.05,
# w_AIC = 0, w_C.Index = 0, w_AUC = 1, w_I.BRIER = 0, times = NULL, max_time_points = 15,
# MIN_AUC_INCREASE = 0.01, MIN_AUC = 0.8, MIN_COMP_TO_CHECK = 3,
# pred.attr = "mean", pred.method = "cenROC", fast_mode = F,
# MIN_EPV = 5, return_models = F, remove_non_significant = F, returnData = F,
# PARALLEL = F, verbose = F, seed = 123)
#
# cv.sb.splsicox_res
## ---- fig.small=T, eval=F-----------------------------------------------------
# cv.sb.splsicox_res$plot_AUC
## -----------------------------------------------------------------------------
sb.splsicox_model <- sb.splsicox(X = X_train, Y = Y_train,
n.comp = 1, #cv.sb.splsicox_res$opt.comp,
penalty = 0.9, #cv.sb.splsicox_res$opt.penalty,
x.center = x.center, x.scale = x.scale,
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)
sb.splsicox_model
## -----------------------------------------------------------------------------
sb.splsicox_model <- sb.splsicox(X = X_train, Y = Y_train,
n.comp = 1, #cv.sb.splsicox_res$opt.comp,
penalty = 0.9, #cv.sb.splsicox_res$opt.penalty,
x.center = x.center, x.scale = x.scale,
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)
sb.splsicox_model
## ----warning=T, eval=F--------------------------------------------------------
# cv.isb.splsicox_res <- cv.isb.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 = x.center, x.scale = x.scale,
# 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)
#
# cv.isb.splsicox_res
## ---- fig.small=T, eval=F-----------------------------------------------------
# cv.isb.splsicox_res$list_cv_spls_models$mirna$plot_AUC
# cv.isb.splsicox_res$list_cv_spls_models$proteomic$plot_AUC
## ---- warning=F, eval=F-------------------------------------------------------
# isb.splsicox_model <- isb.splsicox(X = X_train, Y = Y_train, cv.isb = cv.isb.splsicox_res,
# x.center = x.center, x.scale = x.scale,
# remove_near_zero_variance = TRUE, remove_zero_variance = TRUE, toKeep.zv = NULL,
# remove_non_significant = FALSE, alpha = 0.05,
# MIN_EPV = 5, returnData = TRUE, verbose = FALSE)
#
# isb.splsicox_model
## ---- warning=F, eval=F-------------------------------------------------------
# cv.sb.splsdrcox_res <- cv.sb.splsdrcox(X = X_train, Y = Y_train,
# max.ncomp = 2, vector = NULL,
# n_run = 2, k_folds = 10,
# x.center = x.center, x.scale = x.scale,
# #y.center = FALSE, y.scale = FALSE,
# 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)
#
# cv.sb.splsdrcox_res
## -----------------------------------------------------------------------------
sb.splsdrcox_model <- sb.splsdrcox(X = X_train,
Y = Y_train,
n.comp = 2, #cv.sb.splsdrcox_res$opt.comp,
vector = list("mirna" = 484, "proteomic" = 369), #cv.sb.splsdrcox_res$opt.nvar,
x.center = x.center, x.scale = x.scale,
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)
sb.splsdrcox_model
## ----warning=T, eval=F--------------------------------------------------------
# cv.isb.splsdrcox_res <- cv.isb.splsdrcox(X = X_train, Y = Y_train,
# max.ncomp = 2, vector = NULL,
# n_run = 2, k_folds = 5,
# x.center = x.center, x.scale = x.scale,
# 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)
#
# cv.isb.splsdrcox_res
## ---- fig.small=T, eval=F-----------------------------------------------------
# cv.isb.splsdrcox_res$list_cv_spls_models$mirna$plot_AUC
# cv.isb.splsdrcox_res$list_cv_spls_models$proteomic$plot_AUC
## ---- warning=F, eval=F-------------------------------------------------------
# isb.splsdrcox_model <- isb.splsdrcox(X = X_train, Y = Y_train, cv.isb = cv.isb.splsdrcox_res,
# x.center = x.center, x.scale = x.scale,
# remove_near_zero_variance = TRUE, remove_zero_variance = TRUE, toKeep.zv = NULL,
# remove_non_significant = FALSE, alpha = 0.05,
# MIN_EPV = 5, returnData = TRUE, verbose = FALSE)
#
# isb.splsdrcox_model
## ---- warning=F, eval=F-------------------------------------------------------
# cv.mb.splsdrcox_res <- cv.mb.splsdrcox(X = X_train, Y = Y_train,
# max.ncomp = 2, vector = NULL, #NULL - autodetection
# MIN_NVAR = 10, MAX_NVAR = NULL, n.cut_points = 10, EVAL_METHOD = "AUC",
# n_run = 2, k_folds = 4,
# x.center = x.center, x.scale = x.scale,
# 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)
#
# cv.mb.splsdrcox_res
## -----------------------------------------------------------------------------
mb.splsdrcox_model <- mb.splsdrcox(X = X_train, Y = Y_train,
n.comp = 2, #cv.mb.splsdrcox_res$opt.comp,
vector = list("mirna" = 326, "proteomic" = 369), #cv.mb.splsdrcox_res$opt.nvar,
x.center = x.center, x.scale = x.scale,
remove_near_zero_variance = T, remove_zero_variance = T, toKeep.zv = NULL,
remove_non_significant = T, alpha = 0.05,
MIN_AUC_INCREASE = 0.01,
pred.method = "cenROC", max.iter = 200,
times = NULL, max_time_points = 15,
MIN_EPV = 5, returnData = T, verbose = F)
mb.splsdrcox_model
## ---- warning=F, eval=F-------------------------------------------------------
# # run cv.splsdrcox
# cv.mb.splsdacox_res <- cv.mb.splsdacox(X = X_train, Y = Y_train,
# max.ncomp = 2, vector = NULL, #NULL - autodetection
# n_run = 2, k_folds = 4,
# x.center = x.center, x.scale = x.scale,
# 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)
#
# cv.mb.splsdacox_res
## -----------------------------------------------------------------------------
mb.splsdacox_model <- mb.splsdacox(X = X_train, Y = Y_train,
n.comp = 2, #cv.mb.splsdacox_res$opt.comp,
vector = list("mirna" = 326, "proteomic" = 10), #cv.mb.splsdacox_res$opt.nvar,
x.center = x.center, x.scale = x.scale,
remove_near_zero_variance = T, remove_zero_variance = T, toKeep.zv = NULL,
remove_non_significant = T, alpha = 0.05,
MIN_AUC_INCREASE = 0.01,
pred.method = "cenROC", max.iter = 200,
times = NULL, max_time_points = 15,
MIN_EPV = 5, returnData = T, verbose = F)
mb.splsdacox_model
## -----------------------------------------------------------------------------
lst_models <- list("SB.sPLS-ICOX" = sb.splsicox_model,
#"iSB.sPLS-ICOX" = isb.splsicox_model,
"SB.sPLS-DRCOX-Dynamic" = sb.splsdrcox_model,
#"iSB.sPLS-DRCOX-Dynamic" = isb.splsdrcox_model,
#"SB.sPLS-DRCOX-Penalty" = sb.splsdrcox_penalty_model,
#"iSB.sPLS-DRCOX-Penalty" = isb.splsdrcox_penalty_model,
#"SB.sPLS-DACOX-Dynamic" = sb.splsdacox_model,
#"iSB.sPLS-DACOX-Dynamic" = isb.splsdacox_model,
"MB.sPLS-DRCOX" = mb.splsdrcox_model,
"MB.sPLS-DACOX" = mb.splsdacox_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")
lst_eval_results_brier <- plot_evaluation(eval_results, evaluation = "IBS")
## ---- fig.small=T, warning=F--------------------------------------------------
lst_eval_results$lst_plots$lineplot.mean
lst_eval_results$lst_plot_comparisons$t.test
# lst_eval_results$cenROC$lst_plots$lineplot.mean
# lst_eval_results$cenROC$lst_plot_comparisons$t.test
## -----------------------------------------------------------------------------
lst_models_time <- list(#cv.sb.splsicox_res,
sb.splsicox_model,
#isb.splsicox_model,
#cv.sb.splsdrcox_res,
sb.splsdrcox_model,
#isb.splsdrcox_model,
#cv.mb.splsdrcox_res,
mb.splsdrcox_model,
#cv.mb.splsdrcox_res,
mb.splsdacox_model,
eval_results)
## -----------------------------------------------------------------------------
ggp_time <- plot_time.list(lst_models_time, txt.x.angle = 90)
## ---- fig.small=T-------------------------------------------------------------
ggp_time
## -----------------------------------------------------------------------------
#lst_forest_plot <- plot_forest.list(lst_models)
lst_forest_plot <- plot_forest(lst_models$`SB.sPLS-DRCOX`)
## ---- fig.small=T-------------------------------------------------------------
#lst_forest_plot$`SB.sPLS-DRCOX`
lst_forest_plot
## -----------------------------------------------------------------------------
#lst_ph_ggplot <- plot_proportionalHazard.list(lst_models)
lst_ph_ggplot <- plot_proportionalHazard(lst_models$`SB.sPLS-DRCOX`)
## ---- fig.small=T-------------------------------------------------------------
#lst_ph_ggplot$`SB.sPLS-DRCOX`
lst_ph_ggplot
## -----------------------------------------------------------------------------
#density.plots.lp <- plot_cox.event.list(lst_models, type = "lp")
density.plots.lp <- plot_cox.event(lst_models$`SB.sPLS-DRCOX`, type = "lp")
## ---- fig.small=T-------------------------------------------------------------
density.plots.lp$plot.density
density.plots.lp$plot.histogram
## -----------------------------------------------------------------------------
ggp_scores <- plot_PLS_Coxmos(model = lst_models$`SB.sPLS-DRCOX`,
comp = c(1,2), mode = "scores")
## ---- fig.small=T, warning=FALSE----------------------------------------------
ggp_scores$plot_block
## -----------------------------------------------------------------------------
ggp_loadings <- plot_PLS_Coxmos(model = lst_models$`SB.sPLS-DRCOX`,
comp = c(1,2), mode = "loadings",
top = 10)
## ---- fig.small=T, warning=FALSE----------------------------------------------
ggp_loadings$plot_block
## -----------------------------------------------------------------------------
ggp_biplot <- plot_PLS_Coxmos(model = lst_models$`SB.sPLS-DRCOX`,
comp = c(1,2), mode = "biplot",
top = 15,
only_top = T)
## ---- fig.small=T, warning=FALSE----------------------------------------------
ggp_biplot$plot_block
## ---- warning=F---------------------------------------------------------------
variable_auc_results <- eval_Coxmos_model_per_variable(model = lst_models$`SB.sPLS-DRCOX`,
X_test = lst_models$`SB.sPLS-DRCOX`$X_input,
Y_test = lst_models$`SB.sPLS-DRCOX`$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
## ---- warning=FALSE-----------------------------------------------------------
# ggp.simulated_beta <- plot_pseudobpenalty.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$`SB.sPLS-DRCOX`,
error.bar = T, onlySig = T, alpha = 0.05,
zero.rm = T, auto.limits = T, top = 20,
show_percentage = T, size_percentage = 2)
## ---- fig.small=T, warning=FALSE----------------------------------------------
ggp.simulated_beta$plot
## ---- fig.small=T, warning=FALSE----------------------------------------------
ggp.simulated_beta$mb_plot$plot
## ---- warning=F---------------------------------------------------------------
# LST_KM_RES_LP <- getAutoKM.list(type = "LP",
# lst_models = lst_models,
# comp = 1:4,
# top = 10,
# ori_data = T,
# BREAKTIME = NULL,
# only_sig = T, alpha = 0.05)
LST_KM_RES_LP <- getAutoKM(type = "LP",
model = lst_models$`SB.sPLS-DRCOX`,
comp = 1:4,
top = 10,
ori_data = T,
BREAKTIME = NULL,
only_sig = T, alpha = 0.05)
## ---- fig.small=T, warning=FALSE----------------------------------------------
LST_KM_RES_LP$LST_PLOTS$LP
## ---- warning=FALSE-----------------------------------------------------------
# lst_cutoff <- getCutoffAutoKM.list(LST_KM_RES_LP)
# LST_KM_TEST_LP <- getTestKM.list(lst_models = lst_models,
# X_test = X_test, Y_test = Y_test,
# type = "LP",
# BREAKTIME = NULL, n.breaks = 20,
# lst_cutoff = lst_cutoff)
lst_cutoff <- getCutoffAutoKM(LST_KM_RES_LP)
LST_KM_TEST_LP <- getTestKM(model = lst_models$`SB.sPLS-DRCOX`,
X_test = X_test, Y_test = Y_test,
type = "LP",
BREAKTIME = NULL, n.breaks = 20,
cutoff = lst_cutoff)
## ---- warning=FALSE-----------------------------------------------------------
LST_KM_TEST_LP
## ---- warning=F---------------------------------------------------------------
# LST_KM_RES_COMP <- getAutoKM.list(type = "COMP",
# lst_models = lst_models,
# comp = 1:4,
# top = 10,
# ori_data = T,
# BREAKTIME = NULL,
# only_sig = T, alpha = 0.05)
LST_KM_RES_COMP <- getAutoKM(type = "COMP",
model = lst_models$`SB.sPLS-DRCOX`,
comp = 1:4,
top = 10,
ori_data = T,
BREAKTIME = NULL,
only_sig = T, alpha = 0.05)
## ---- fig.small=T, warning=FALSE----------------------------------------------
LST_KM_RES_COMP$LST_PLOTS$mirna$comp_2
LST_KM_RES_COMP$LST_PLOTS$proteomic$comp_1
LST_KM_RES_COMP$LST_PLOTS$proteomic$comp_2
## ---- warning=F---------------------------------------------------------------
# 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$`SB.sPLS-DRCOX`,
X_test = X_test, Y_test = Y_test,
type = "COMP",
BREAKTIME = NULL, n.breaks = 20,
cutoff = lst_cutoff)
## ---- fig.small=T, warning=FALSE----------------------------------------------
LST_KM_TEST_COMP$comp_2_mirna
LST_KM_TEST_COMP$comp_1_proteomic
LST_KM_TEST_COMP$comp_2_proteomic
## ---- warning=F---------------------------------------------------------------
# LST_KM_RES_VAR <- getAutoKM.list(type = "VAR",
# lst_models = lst_models,
# comp = 1:4,
# top = 10,
# ori_data = T,
# BREAKTIME = NULL,
# only_sig = T, alpha = 0.05)
LST_KM_RES_VAR <- getAutoKM(type = "VAR",
model = lst_models$`SB.sPLS-DRCOX`,
comp = 1:4,
top = 10,
ori_data = T,
BREAKTIME = NULL,
only_sig = T, alpha = 0.05)
## ---- fig.small=T, warning=FALSE----------------------------------------------
LST_KM_RES_VAR$LST_PLOTS$mirna$hsa.minus.miR.minus.21.minus.5p
LST_KM_RES_VAR$LST_PLOTS$proteomic$var_840
LST_KM_RES_VAR$LST_PLOTS$proteomic$var_7535
## ---- warning=FALSE-----------------------------------------------------------
# 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$`SB.sPLS-DRCOX`,
X_test = X_test, Y_test = Y_test,
type = "VAR", ori_data = T,
BREAKTIME = NULL, n.breaks = 20,
cutoff = lst_cutoff)
## ---- fig.small=T, warning=FALSE----------------------------------------------
LST_KM_TEST_VAR$mirna$hsa.minus.miR.minus.21.minus.5p
LST_KM_TEST_VAR$proteomic$var_840
LST_KM_TEST_VAR$proteomic$var_7535
## -----------------------------------------------------------------------------
new_pat <- list()
for(b in names(X_test)){
new_pat[[b]] <- X_test[[b]][1,,drop=F]
}
## -----------------------------------------------------------------------------
knitr::kable(Y_test[rownames(new_pat$mirna),])
## ---- warning=FALSE-----------------------------------------------------------
# ggp.simulated_beta_newPat <- plot_observation.pseudobeta.list(lst_models = lst_models,
# 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_newPat <- plot_observation.pseudobeta(model = lst_models$`SB.sPLS-DRCOX`,
observation = new_pat,
error.bar = T, onlySig = T, alpha = 0.05,
zero.rm = T, auto.limits = T, show.betas = T,
top = 20, txt.x.angle = 90)
## ---- fig.small=T, warning=FALSE----------------------------------------------
ggp.simulated_beta_newPat$plot$mirna
ggp.simulated_beta_newPat$plot$proteomic
## ---- warning=FALSE-----------------------------------------------------------
pat_density <- plot_observation.eventDensity(observation = new_pat,
model = lst_models$`SB.sPLS-DRCOX`,
time = NULL,
type = "lp")
## ---- fig.small=T, warning=FALSE----------------------------------------------
pat_density
## -----------------------------------------------------------------------------
pat_histogram <- plot_observation.eventHistogram(observation = new_pat,
model = lst_models$`SB.sPLS-DRCOX`,
time = NULL,
type = "lp")
## ---- fig.small=T, warning=FALSE----------------------------------------------
pat_histogram
## -----------------------------------------------------------------------------
sub_X_test <- list()
for(b in names(X_test)){
sub_X_test[[b]] <- X_test[[b]][1:5,]
}
## -----------------------------------------------------------------------------
knitr::kable(Y_test[rownames(sub_X_test$proteomic),])
## ---- warning=FALSE-----------------------------------------------------------
# lst_cox.comparison <- plot_multipleObservations.LP.list(lst_models = lst_models,
# observations = sub_X_test,
# error.bar = T, zero.rm = T, onlySig = T,
# alpha = 0.05, top = 5)
lst_cox.comparison <- plot_multipleObservations.LP(model = lst_models$`SB.sPLS-DRCOX`,
observations = sub_X_test,
error.bar = T, zero.rm = T, onlySig = T,
alpha = 0.05, top = 5)
## ---- fig.small=T, warning=FALSE----------------------------------------------
lst_cox.comparison$plot