Extracts the Confidence Intervals for the Coefficients for the 'Adapted Paik et Al.' Model

Extracts the confidence intervals for \(\boldsymbol{\beta}\) obtained with the time-dependent frailty model proposed in the 'Adapted Paik et al.' framework.

Usage

# S3 method for class 'AdPaik'
confint(object, parm = NULL, level = 0.95, ...)

Arguments

object: An S3 object of class AdPaik, returned by the main model function (AdPaikModel). This object contains all the optimal parameter estimates.
parm: A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. Defaults to NULL, and all parameters are considered. Changing it is not supported for this model. It will be ignored.
level: The confidence level required. Defaults to 0.95.
...: Additional arguments to be passed to other methods.

Value

A named list containing the categories of the standard errors for the optimal parameters.

Details

The confint.AdPaik function extracts the standard errors for the beta coefficients from the ParametersCI field in object.

The function validates the structure of object and ensures compatibility with the expected model output. It throws an error if the object is malformed or inconsistent.

Examples

# Example using the 'Academic Dropout' dataset
data(data_dropout)

# Define the formula and time axis for the model
formula <- time_to_event ~ Gender + CFUP + cluster(group)
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)
eps <- 1e-10
categories_range_min <- c(-8, -2, eps, eps, eps)
categories_range_max <- c(-eps, 0, 1 - eps, 1, 10)

# \donttest{
# Run the main model
result <- AdPaikModel(formula, data_dropout, time_axis,
                      categories_range_min, categories_range_max, TRUE)
#> 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

# Extract the coefficients
confint(result)
#> Error: object 'result' not found
# }