fitCF_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.

```
fitCF_MHmcmc(
result = stop("An output from fitCF() must be provided"),
n.iter = 10000,
parametersMCMC = stop("A parameter set from fitCF_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. |

fitCF_MHmcmc runs the Metropolis-Hastings algorithm for ECFOCF (Bayesian MCMC)

A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used

Marc Girondot marc.girondot@gmail.com

Other Model of Clutch Frequency:
`ECFOCF_f()`

,
`ECFOCF_full()`

,
`TableECFOCF()`

,
`fitCF()`

,
`fitCF_MHmcmc_p()`

,
`generateCF()`

,
`lnLCF()`

,
`logLik.ECFOCF()`

,
`plot.ECFOCF()`

,
`plot.TableECFOCF()`

```
## Not run:
library("phenology")
data(MarineTurtles_2002)
ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002)
# Paraetric model for clutch frequency
o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539,
sd = 1.1465246643327098,
p = 0.25785366120357966),
fixed.parameters=NULL,
data=ECFOCF_2002, hessian = TRUE)
pMCMC <- fitCF_MHmcmc_p(result=o_mu1p1_CFp, accept=TRUE)
fitCF_MCMC <- fitCF_MHmcmc(result = o_mu1p1_CFp, n.iter = 1000,
parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0,
adaptive=TRUE,
thin = 1, trace = TRUE)
plot(fitCF_MCMC, parameters="mu")
plot(fitCF_MCMC, parameters="sd")
plot(fitCF_MCMC, parameters="p", xlim=c(0, 0.5), breaks=seq(from=0, to=0.5, by=0.05))
plot(fitCF_MCMC, parameters="p", transform = invlogit, xlim=c(0, 1),
breaks=c(seq(from=0, to=1, by=0.05)))
## End(Not run)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.