| as.parameters {HelpersMG} | R Documentation |
Extract parameters from mcmcComposite object
Description
Take a mcmcComposite object and create a vector object with parameter value at specified iteration.
If index="best", the function will return the parameters for the highest likelihood. It also indicates at which iteration the maximum lihelihood has been observed.
If index="last", the function will return the parameters for the last likelihood.
If index="median", the function will return the median value of the parameter.
if index="quantile", the function will return the probs defined by quantiles parameter.
If index="mode", the function will return the mode value of the parameter based on Asselin de Beauville (1978) method.
index can also be a numeric value.
This function uses the complete iterations available except the adaptation part,
even if thin parameter is not equal to 1.
Usage
as.parameters(x, index = "best", chain = 1, probs = 0.025)
Arguments
x |
A mcmcComposite obtained as a result of |
index |
At which iteration the parameters must be taken, see description |
chain |
The number of the chain in which to get parameters |
probs |
Quantiles to be returned, see description |
Value
A vector with parameters at maximum likelihood or index position
Author(s)
Marc Girondot marc.girondot@gmail.com
References
Asselin de Beauville J.-P. (1978). Estimation non paramétrique de la densité et du mode, exemple de la distribution Gamma. Revue de Statistique Appliquée, 26(3):47-70.
See Also
Other mcmcComposite functions:
MHalgoGen(),
as.mcmc.mcmcComposite(),
as.quantiles(),
merge.mcmcComposite(),
plot.PriorsmcmcComposite(),
plot.mcmcComposite(),
setPriors(),
summary.mcmcComposite()
Examples
## Not run:
library(HelpersMG)
require(coda)
x <- rnorm(30, 10, 2)
dnormx <- function(data, x) {
data <- unlist(data)
return(-sum(dnorm(data, mean=x['mean'], sd=x['sd'], log=TRUE)))
}
parameters_mcmc <- data.frame(Density=c('dnorm', 'dlnorm'),
Prior1=c(10, 0.5),
Prior2=c(2, 0.5),
SDProp=c(1, 1),
Min=c(-3, 0),
Max=c(100, 10),
Init=c(10, 2),
stringsAsFactors = FALSE,
row.names=c('mean', 'sd'))
mcmc_run <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x,
likelihood=dnormx, n.chains=1, n.adapt=100,
thin=1, trace=1)
plot(mcmc_run, xlim=c(0, 20))
plot(mcmc_run, xlim=c(0, 10), parameters="sd")
mcmcforcoda <- as.mcmc(mcmc_run)
#' heidel.diag(mcmcforcoda)
raftery.diag(mcmcforcoda)
autocorr.diag(mcmcforcoda)
acf(mcmcforcoda[[1]][,"mean"], lag.max=20, bty="n", las=1)
acf(mcmcforcoda[[1]][,"sd"], lag.max=20, bty="n", las=1)
batchSE(mcmcforcoda, batchSize=100)
# The batch standard error procedure is usually thought to
# be not as accurate as the time series methods used in summary
summary(mcmcforcoda)$statistics[,"Time-series SE"]
summary(mcmc_run)
as.parameters(mcmc_run)
lastp <- as.parameters(mcmc_run, index="last")
parameters_mcmc[,"Init"] <- lastp
# The n.adapt set to 1 is used to not record the first set of parameters
# then it is not duplicated (as it is also the last one for
# the object mcmc_run)
mcmc_run2 <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x,
likelihood=dnormx, n.chains=1, n.adapt=1,
thin=1, trace=1)
mcmc_run3 <- merge(mcmc_run, mcmc_run2)
####### no adaptation, n.adapt must be 0
parameters_mcmc[,"Init"] <- c(mean(x), sd(x))
mcmc_run3 <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x,
likelihood=dnormx, n.chains=1, n.adapt=0,
thin=1, trace=1)
# With index being median, it returns the median value for each parameter
as.parameters(mcmc_run3, index="median")
as.parameters(mcmc_run3, index="mode")
as.parameters(mcmc_run3, index="best")
as.parameters(mcmc_run3, index="quantile", probs=0.025)
as.parameters(mcmc_run3, index="quantile", probs=0.975)
as.parameters(mcmc_run3, index="quantile", probs=c(0.025, 0.975))
## End(Not run)