plot_multipleObservations.LP.list function

plot_multipleObservations.LP.list

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

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).

Returns

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.

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,])

Author(s)

Pedro Salguero Garcia. Maintainer: pedsalga@upv.edu.es