Plot of Conditional Survival Function

Plots the conditional survival function based on the 'Adapted Paik et al.' model's estimated coefficients and frailty effects, for each unit in each time interval (represented by its mid point).

Usage

plot_survivalAdPaik(
  result,
  lwd = 1,
  xlim = c(min(result$TimeDomain), max(result$TimeDomain)),
  ylim = c(0, 1),
  xlab = "Time",
  ylab = "Values",
  main = "Conditional Survival",
  cex = 0.2,
  cexlegend = 0.8
)

Arguments

result

S3 object of class 'AdPaik' containing model results.

lwd

The line width of the plot. Default is 1.

xlim

A numeric vector specifying the range for the x-axis (intervals). Default is min-max value of the time domain.

ylim

A numeric vector specifying the range for the y-axis (intervals). Default is the range 0-1.

xlab, ylab

String giving the x and y axis name. Default values are 'Time' and 'Values'.

main

Title of the plot. Default title is 'Survival'.

cex

Dimension of the points used for plotting the estimates. Defaults to 0.2.

cexlegend

Dimension of the text used for the legend. Defaults to 0.9.

Value

The plot of the conditional survival function.

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
# \donttest{
result <- AdPaikModel(formula, data_dropout, time_axis, categories_range_min, categories_range_max)
#> Error in while (r <= n_run & actual_tol_ll > tol_ll) {    if (verbose)         message(paste("Run ", r))    RemainingIndexes <- RunIndexes[r, ]    UsedIndexes <- c()    while (length(RemainingIndexes) != 0) {        index_to_vary <- RemainingIndexes[1]        PosIndex <- which(RemainingIndexes == index_to_vary)        RemainingIndexes <- RemainingIndexes[-PosIndex]        UsedIndexes <- c(UsedIndexes, index_to_vary)        result_optimize <- suppressWarnings(optimize(ll_AdPaik_1D,             c(params_range_min[index_to_vary], params_range_max[index_to_vary]),             maximum = TRUE, tol = tol_optimize, index_to_vary,             params, dataset, centre, time_axis, dropout_matrix,             e_matrix))        params[index_to_vary] <- result_optimize$maximum    }    global_optimal_params[r, ] <- params    global_optimal_loglikelihood_run <- ll_AdPaik_eval(params,         dataset, centre, time_axis, dropout_matrix, e_matrix)    global_optimal_loglikelihood[r] <- global_optimal_loglikelihood_run    if (is.nan(global_optimal_loglikelihood_run))         stop("NaN value for the optimal log-likelihood value.")    if (print_previous_ll_values[1]) {        n_previous <- print_previous_ll_values[2]        if (r < n_previous)             if (verbose)                 message(paste(" Global log-likelihood: ", global_optimal_loglikelihood[1:r]))            else if (verbose)                 message(paste(" Global log-likelihood: ", global_optimal_loglikelihood[(r -                   n_previous + 1):r]))    }    actual_tol_ll <- abs(ll_optimal - global_optimal_loglikelihood_run)    if (ll_optimal < global_optimal_loglikelihood_run) {        ll_optimal <- global_optimal_loglikelihood_run        optimal_run <- r    }    r <- r + 1}: missing value where TRUE/FALSE needed

plot_survivalAdPaik(result)
#> Error: object 'result' not found
 # }