Compute the Conditional Survival Function

Computes the conditional survival function based on the 'Adapted Paik et al.' model's given the estimated coefficients and frailty effects.

Usage

survivalAdPaik(result)

Arguments

result

S3 object of class 'AdPaik' containing model results.

Value

A dataset where each row corresponds to an individual unit in the dataset, and the columns represent the survival function values over time interval, with the first column indicating the cluster to which the individual belongs.

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

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