inter_frailty_cl: Cure rate frailty model for interval censored clustered data
In intercure: Cure Rate Estimators for Interval Censored Data

Description Usage Arguments Value Examples

inter_frailty_cl returns a list with the estimated parameters par and their covariance matrix mcov. The list also contains a dummy variable stop_c assuming 0 if algorithm converged and 1 if a stop criteria ended the process.

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inter_frailty_cl(dataset, left, right, delta, cov_theta, cov_beta, grp, M,
  b = 0.001, tol = 0.001, max_n = 100, par_cl = NULL, burn_in = 50,
  output_files = FALSE)

`dataset`	Dataset used to fit the model.
`left`	Vector containing the last check times before event.
`right`	Vector containing the first check times after event.
`delta`	Flag vector indicating failure inside interval.
`cov_theta`	String vector containing the column names to be used on the cure rate predictor.
`cov_beta`	String vector containing the column names to be used on the predictor associated with the hazard function.
`grp`	Vector containing cluster identifier (numeric or string).
`M`	Number of replicates generated by each iteration on the ANDA (Asymptotic Normal Data Augmentation) algorithm.
`b`	Parameter for initial theta and beta variances.
`tol`	Numeric for tolerance of convergence.
`max_n`	Maximum number of algorithm's iterations without the burn in.
`par_cl`	Registered SOCK cluster for parallel process. If NULL (default) the program loops are executed sequentially.
`burn_in`	Number of burn in iterations.
`output_files`	Boolean indicating if text outputs for the estimates and variances should be generated.

The inter_frailty_cl function returns an list containing the following outputs:

`par`	estimates of theta and beta parameters.
`mcov`	estimates for the covariance matrix of theta and beta parameters.
`stop_c`	stop criteria indicator assuming 1 when process is stopped for a non-convergence criteria. Assumes 0 when convergence is reached.

set.seed(3)
sample_set <- sim_frailty_cl(80, nclus = 3)

## few iterations just to check how to use the function

inter_frailty_cl(sample_set, sample_set$L, sample_set$R, sample_set$delta,
c("xi1"), c("xi1"), grp = sample_set$clus, M = 20,
max_n = 2, burn_in = 0)

## precise estimate (computationally intensive)
## Not run: 

inter_frailty_cl(sample_set, sample_set$L, sample_set$R, sample_set$delta,
c("xi1", "xi2"), c("xi1", "xi2"), grp = sample_set$clus, M = 20,
max_n = 30, burn_in = 10)

## End(Not run)