updateGCMM {activityGCMM} | R Documentation |
Extend GCMM analysis using extend.jags
from package runjags
updateGCMM( model, burnin = 0, sample = 10000, saveclustIDs = FALSE, saveREs = FALSE, saveResids = FALSE, autorun = TRUE, minESS = 5000, maxrep = 5, drop.chain = 0 )
model |
Object of class |
burnin |
Number of iterations per MCMC chain to be discarded as a burn-in |
sample |
Number of iterations per MCMC chain |
saveclustIDs |
Whether to save component cluster identification for the data points; default=FALSE |
saveREs |
Whether random intercepts are saved in output; recommended to save only one of saveREs, saveResids or saveYExp at one time due to memory limitations; default=FALSE |
saveResids |
Whether model residuals are saved in output; recommended to save only one of saveREs, saveResids or saveYExp at one time due to memory limitations |
autorun |
Whether to automatically extend the analysis until MCMC chain convergence and minimum effective sample size (ESS) is achieved; default is TRUE |
minESS |
Desired minimum effective sample size (MCMC) when automatically extending the analysis using |
maxrep |
Maximum number of times to automatically extend the analysis if MCMC chains have not converged or the minimum effective sample size is not reached; default=5 |
drop.chain |
A number indicating which MCMC chain to drop from the updated analysis. This may be useful if one chain happens to converge on opposite clusters than the others. |
Returns an object of class GCMM
with a list of analysis details and output; see GCMM
. A mixture plot of the estimated activity curve is also printed.
FoxActivityGCMM<-GCMM(data=redfoxsample$Radians, RE1=redfoxsample$SamplingPeriod, family="vonmises", autorun=FALSE, adapt=0, sample=300, burnin=300, thin=1, n.chains=2) updateFoxGCMM<-updateGCMM(FoxActivityGCMM, sample=300, autorun=FALSE)