HelpersMG (version 5.0)

as.parameters: 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="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)

Arguments

x

A mcmcComposite obtained as a result of MHalgoGen() function

index

At which iteration the parameters must be taken, see description

chain

The number of the chain in which to get parameters

Value

A vector with parameters at maximum likelihood or index position

References

Asselin de Beauville J.-P. (1978). Estimation non param<U+00E9>trique de la densit<U+00E9> et du mode, exemple de la distribution Gamma. Revue de Statistique Appliqu<U+00E9>e, 26(3):47-70.

See Also

Other mcmcComposite functions: MHalgoGen(), as.mcmc.mcmcComposite(), as.quantiles(), merge.mcmcComposite(), plot.mcmcComposite(), 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")
# }