plot_multipleObservations.LP.list: plot_multipleObservations.LP.list

In Coxmos: Cox MultiBlock Survival

plot_multipleObservations.LP.list

Description

Run the function "plot_multipleObservations.LP" for a list of models. More information in "?plot_multipleObservations.LP".

Usage

plot_multipleObservations.LP.list(
  lst_models,
  observations,
  error.bar = FALSE,
  onlySig = TRUE,
  alpha = 0.05,
  zero.rm = TRUE,
  txt.x.angle = 0,
  title = NULL,
  subtitle = NULL,
  legend.position = "bottom",
  auto.limits = TRUE,
  top = NULL
)

Arguments

lst_models	List of Coxmos models.
observations	Numeric matrix or data.frame. New explanatory variables (raw data). Qualitative variables must be transform into binary variables.
error.bar	Logical. Show error bar (default: FALSE).
onlySig	Logical. Compute plot using only significant components (default: TRUE).
alpha	Numeric. Numerical values are regarded as significant if they fall below the threshold (default: 0.05).
zero.rm	Logical. Remove variables equal to 0 (default: TRUE).
txt.x.angle	Numeric. Angle of X text (default: 0).
title	Character. Plot title (default: NULL).
subtitle	Character. Plot subtitle (default: NULL).
legend.position	Character. Legend position. Must be one of the following: "top", "bottom", "right" or "left (default: "bottom").
auto.limits	Logical. If "auto.limits" = TRUE, limits are detected automatically (default: TRUE).
top	Numeric. Show "top" first variables. If top = NULL, all variables are shown (default: NULL).

Value

A list of ggplot objects for each model in the lst_models. Each plot visualizes the linear predictor values for multiple patients based on the specified Coxmos model. The plots can optionally display error bars, consider only significant components, and can be limited to a specified number of top variables. The visualization aids in understanding the influence of explanatory variables on the survival prediction for each patient in the context of the provided models.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

Examples

data("X_proteomic")
data("Y_proteomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .4, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:30]
Y_train <- Y_proteomic[index_train,]
X_test <- X_proteomic[-index_train,1:30]
Y_test <- Y_proteomic[-index_train,]
splsicox.model <- splsicox(X_train, Y_train, n.comp = 1, penalty = 0.5, x.center = TRUE,
x.scale = TRUE)
splsdrcox.model <- splsdrcox_penalty(X_train, Y_train, n.comp = 1, penalty = 0.5, x.center = TRUE,
x.scale = TRUE)
lst_models = list("sPLSICOX" = splsicox.model, "sPLSDRCOX" = splsdrcox.model)
plot_multipleObservations.LP.list(lst_models = lst_models, X_test[1:5,])

Coxmos documentation built on April 4, 2025, 12:20 a.m.