| rcutter {HelpersMG} | R Documentation |
Random values of unobserved values of cut distribution.
Description
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.
Usage
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. |
Details
rcutter returns random values based on fitted distribution with cut.
Value
A vector with the random numbers.
Author(s)
Marc Girondot marc.girondot@gmail.com
See Also
Other Distributions:
cutter(),
dSnbinom(),
dbeta_new(),
dcutter(),
dggamma(),
logLik.cutter(),
plot.cutter(),
print.cutter(),
r2norm(),
rmnorm(),
rnbinom_new()
Examples
## 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<DL] <- -Inf
# search for the parameters the best fit these truncated data
result <- cutter(observations=obc, lower_detection_limit=DL,
cut_method="censored")
result
plot(result, breaks=seq(from=0, to=200, by=10))
r <- rcutter(cutter=result, n=100)
hist(r, breaks=seq(from=0, to=200, by=10))
r <- rcutter(cutter=result, lower_detection_limit=DL, n=100)
hist(r, breaks=seq(from=0, to=250, by=10))
# With censored method, some values are replaced with +Inf or -Inf
any(is.infinite(r))
r <- rcutter(cutter=result, upper_detection_limit=DL, n=100,
method_cut="truncated")
# With truncated method, the values below LDL or upper UDL are not present
any(is.infinite(r))
hist(r, breaks=seq(from=0, to=10, by=0.25))
r <- rcutter(cutter=result, observed_detection_limit=TRUE)
hist(r, breaks=seq(from=0, to=300, by=10))
## End(Not run)