plot_observation.eventDensity {Coxmos}

plot_observation.eventDensity

Description

Visualizes the event density for a given observation's data using the Coxmos model.

Usage

plot_observation.eventDensity(
  observation,
  model,
  time = NULL,
  type = "lp",
  size = 3,
  color = "red"
)

Arguments

observation

Numeric matrix or data.frame. New explanatory variables (raw data) for one observation. Qualitative variables must be transform into binary variables.

model

Coxmos model.

time

Numeric. Time point where the AUC will be evaluated (default: NULL).

type

Character. Prediction type: "lp", "risk", "expected" or "survival" (default: "lp").

size

Numeric. Point size (default: 3).

color

String. R Color.

Details

The plot_observation.eventDensity function provides a graphical representation of the event density for a specific observation's data, based on the Coxmos model. The function computes the density of events and non-events and plots them, highlighting the predicted value for the given observation's data. The density is determined using density estimation, and the predicted value is obtained from the Coxmos model. The function allows customization of the plot aesthetics, such as point size and color. The resulting plot provides a visual comparison of the observation's predicted event density against the overall event density distribution, aiding in the interpretation of the observation's risk profile.

Value

A ggplot object representing a density of the predicted event values based on the provided Coxmos model.

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 = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:50]
Y_train <- Y_proteomic[index_train,]
X_test <- X_proteomic[-index_train,1:50]
Y_test <- Y_proteomic[-index_train,]
coxEN.model <- coxEN(X_train, Y_train, x.center = TRUE, x.scale = TRUE)
observation = X_test[1,,drop=FALSE]
plot_observation.eventDensity(observation = observation, model = coxEN.model, time = NULL)