plot_post_frailty_est: Plot the Posterior Frailty Estimates

In TimeDepFrail: Time Dependent Shared Frailty Cox Model

Plot the Posterior Frailty Estimates

Description

This function plots the posterior frailty estimates for each group in each time interval (represented by its mid point). Each group's estimates are represented by a sequence of points connected by straight lines. The function can plot either the entire posterior frailty estimate or its time-independent and time-dependent components based on user-specified flags.

Usage

plot_post_frailty_est(
  result,
  flag_eps = FALSE,
  flag_alpha = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = "Time",
  ylab = "Values",
  main = "Posterior frailty estimates",
  cex = 0.7,
  pch_type = seq(1, length(result$ClusterCodes)),
  color_bg = rep("black", length(result$ClusterCodes)),
  cex_legend = 0.7,
  pos_legend = "topright"
)

Arguments

result	S3 object of class 'AdPaik', returned by the method call 'AdPaikModel(...)'.
flag_eps	Logical flag indicating whether to plot only the time-dependent posterior frailty estimates. Default is FALSE.
flag_alpha	Logical flag indicating whether to plot only the time-independent posterior frailty estimates. Default is FALSE.
xlim	A numeric vector specifying the range for the x-axis (intervals). If NULL, default is set to the interval min-max of the time-domain, plus space for the legend. If flag_alpha = TRUE, the plot is produced around 1 (defaults to 0.8-1.4).
ylim	A numeric vector specifying the range for the y-axis (intervals). If NULL, default is min-max value of the posterior frailty estimate.
xlab, ylab	String giving the x and y axis name. Default values are 'Time' and 'Values'.
main	Title of the plot. Default title is 'Posterior frailty estimates'.
cex	Dimension of the points used for plotting the estimates.
pch_type	Numerical vector of length equal to the number of clusters in the data, giving the symbol to be used for plotting the estimates. Default symbol (circle, 21) is the same for all clusters.
color_bg	Numerical vector of length equal to the number of clusters in the data, giving the color to be used for plotting the symbols for the estimates. Default ('black') is the same for all faculties. On the other hand, the same color is used throughout the intervals for the same faculty.
cex_legend	Dimension of the symbol in the legend. Default is 0.7.
pos_legend	Either a numeric vector providing the x and y coordinates for the legend or a string specifying the legend's position (e.g., 'bottomright', 'bottom', 'bottomleft', 'left', 'topleft', 'top', 'topright', 'right', 'center').

Details

Recalling the frailty structure as Z_{jk} = \alpha_{j} + \epsilon_{jk}, \forall j,k and the posterior frailty estimate as \hat{Z}_{jk} = \hat{\alpha}_{j}/\hat{\alpha}_{max} + \hat{\epsilon}_{jk}/\hat{\epsilon}_{max}, this function allows plotting either the entire posterior frailty estimate \hat{Z}_{jk} or its time-independent \frac{\hat{\alpha}_{j}}{\hat{\alpha}_{\text{max}}} or time-dependent \frac{\hat{\epsilon}_{jk}}{\hat{\epsilon}_{\text{max}}} components. The user can control which components to display using the flag_eps and flag_alpha parameters. Only one of these flags can be set to TRUE at a time.

Value

The plot of the posterior frailty estimates.

Examples

# Import data
data(data_dropout)

# Define the variables needed for the model execution
eps_paik <- 1e-10
categories_range_min <- c(-8, -2, eps_paik, eps_paik, eps_paik)
categories_range_max <- c(-eps_paik, 0.4, 1 - eps_paik, 1, 10)
time_axis <- c(1.0, 1.4, 1.8, 2.3, 3.1, 3.8, 4.3, 5.0, 5.5, 5.8, 6.0)
formula <- time_to_event ~ Gender + CFUP + cluster(group)

# Call the main model function


result <- AdPaikModel(formula, data_dropout, time_axis, categories_range_min, categories_range_max)

# Define variables for plotting the estimates
pch_type <- c(21, seq(21,25,1), seq(21,25,1), seq(21,25,1))
color_bg <- c("darkblue", rep("red", 5), rep("purple", 5), rep("green",5))

plot_post_frailty_est(result, pch_type = pch_type, color_bg = color_bg)
                      

TimeDepFrail documentation built on April 11, 2025, 5:41 p.m.