View source: R/fitRMU_MHmcmc.R
fitRMU_MHmcmc | R Documentation |
Run the Metropolis-Hastings algorithm for RMU.data.
The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.
I recommend thin=1 because the method to estimate SE uses resampling.
As initial point is maximum likelihood, n.adapt = 0 is a good solution.
The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename.
The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search.
These options are used to prevent the consequences of computer crash or if the run is very very long and computer processes at time limited.
fitRMU_MHmcmc(
result = stop("An output from fitRMU() must be provided"),
n.iter = 10000,
parametersMCMC = stop("A parameter set from fitRMU_MHmcmc_p() must be provided"),
n.chains = 1,
n.adapt = 0,
thin = 1,
adaptive = FALSE,
adaptive.lag = 500,
adaptive.fun = function(x) {
ifelse(x > 0.234, 1.3, 0.7)
},
trace = FALSE,
traceML = FALSE,
intermediate = NULL,
filename = "intermediate.Rdata",
previous = NULL
)
result |
An object obtained after a SearchR fit |
n.iter |
Number of iterations for each step |
parametersMCMC |
A set of parameters used as initial point for searching with information on priors |
n.chains |
Number of replicates |
n.adapt |
Number of iterations before to store outputs |
thin |
Number of iterations between each stored output |
adaptive |
Should an adaptive process for SDProp be used |
adaptive.lag |
Lag to analyze the SDProp value in an adaptive content |
adaptive.fun |
Function used to change the SDProp |
trace |
TRUE or FALSE or period, shows progress |
traceML |
TRUE or FALSE to show ML |
intermediate |
Period for saving intermediate result, NULL for no save |
filename |
If intermediate is not NULL, save intermediate result in this file |
previous |
Previous result to be continued. Can be the filename in which intermediate results are saved. |
fitRMU_MHmcmc runs the Metropolis-Hastings algorithm for RMU.data (Bayesian MCMC)
A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used
Marc Girondot
Other Fill gaps in RMU:
CI.RMU()
,
fitRMU()
,
fitRMU_MHmcmc_p()
,
logLik.fitRMU()
,
plot.fitRMU()
## Not run:
library("phenology")
RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana",
"Galibi.Suriname",
"Irakumpapy.French.Guiana"),
se=c("se_Yalimapo.French.Guiana",
"se_Galibi.Suriname",
"se_Irakumpapy.French.Guiana"))
data.AtlanticW <- data.frame(Year=c(1990:2000),
Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA,
6542, 5678, 1243, NA, 1566, 1566),
se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA,
230, 129, 167, NA, 145, 20),
Galibi.Suriname=c(276, 275, 290, NA, 267,
542, 678, NA, 243, 156, 123),
se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2,
4.3, 2.3, NA, 10.3, 10.1, 8.9),
Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA,
3542, 2678, 243, NA, 566, 566),
se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA,
130, 29, 67, NA, 15, 20))
cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW,
colname.year="Year", model.trend="Constant",
model.SD="Zero")
pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE)
fitRMU_MCMC <- fitRMU_MHmcmc(result = cst, n.iter = 10000,
parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE)
## End(Not run)
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