plot_events {Coxmos}

plot_events

Description

Generates a bar plot visualizing the distribution of events over time, categorizing observations as either censored or non-censored.

Usage

plot_events(
  Y,
  max.breaks = 20,
  roundTo = 0.1,
  categories = c("Censored", "Death"),
  y.text = "Number of observations",
  verbose = FALSE
)

Arguments

Y

Numeric matrix or data.frame. Response variables. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.

max.breaks

Numeric. Maximum number of breaks in X axis (default: 20).

roundTo

Numeric. Value to round time. If roundTo = 0.1, the results will be rounded to the tenths (default: 0.1).

categories

Character vector. Vector of length two to name both categories for censored and non-censored observations (default: c("Censored","Death")).

y.text

Character. Y axis title (default: "Number of observations").

verbose

Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).

Details

The plot_events function is meticulously crafted to provide a visualization of event occurrences over a specified time frame. The primary objective of this function is to elucidate the distribution of events, distinguishing between censored and non-censored observations. The input response matrix, "Y", is expected to encompass two pivotal columns: "time" and "event". The "time" column delineates the temporal occurrence of each observation, while the "event" column demarcates whether an observation is censored or an event, with accepted binary representations being 0/1 or FALSE/TRUE.

The function employs a systematic approach to categorize the time variable into distinct intervals or "breaks". The number of these intervals is determined by the "max.breaks" parameter, and their size is influenced by the "roundTo" parameter. Each interval represents a range of time values, and the resulting plot showcases the number of censored and non-censored observations within each interval. The bars in the plot are color-coded based on the event type, offering a clear visual distinction between the two categories.

Value

A list of two elements. plot: Barplot. df: Data.frame used for the plotting.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

Examples

data("X_proteomic")
data("Y_proteomic")
Y_train <- Y_proteomic
plot_events(Y_train, categories = c("Censored","Death"))