# Random values of unobserved values of cut distribution. Return n random numbers. It can be used to get the posterior predictive distribution; see example. If random_method is "ML", the parameter values obtained using maximum likelihood are used. If random_method is "medianMCMC", the parameter values obtained using median of posterior distribution are used. If random_method is "MCMC", the parameter values are one sample of the MCMC posterior distribution. if observed_detection_limit is set to TRUE, the number of random number is equal to the number of observations; n is not used. rcutter is the abbreviation for random-cutter. ```r rcutter( cutter = stop("A result of cutter() must be provided"), n = 1, lower_detection_limit = NULL, upper_detection_limit = NULL, method_cut = c("censored", "truncated"), observed_detection_limit = FALSE, random_method = c("medianMCMC", "MCMC", "ML"), index_mcmc = NULL ) ``` ## Arguments - `cutter`: The fitted model obtained with cutter() - `n`: number of random numbers - `lower_detection_limit`: The lower detection limit - `upper_detection_limit`: The upper detection limit - `method_cut`: What method is used to cut the distribution: "censored", "truncated"? - `observed_detection_limit`: If TRUE, will use the pattern of detection limit as in observations - `random_method`: How to get parameters; it can be "ML", "medianMCMC", or "MCMC" - `index_mcmc`: For MCMC random_method, the index of data to be used. ## Returns A vector with the random numbers. ## Details rcutter returns random values based on fitted distribution with cut. ## Examples ```r ## Not run: library(HelpersMG) # _______________________________________________________________ # right censored distribution with gamma distribution # _______________________________________________________________ # Detection limit DL <- 100 # Generate 100 random data from a gamma distribution obc <- rgamma(100, scale=20, shape=2) # remove the data below the detection limit obc[obc>DL] <- +Inf # search for the parameters the best fit these censored data result <- cutter(observations=obc, upper_detection_limit=DL, cut_method="censored") result # Posterior predictive distribution r <- rcutter(cutter=result, upper_detection_limit=DL, n=100) hist(r) # _______________________________________________________________ # left censored distribution with gamma distribution # _______________________________________________________________ # Detection limit DL <- 10 # Generate 100 random data from a gamma distribution obc <- rgamma(100, scale=20, shape=2) # remove the data below the detection limit obc[obc

rcutter function