diagnose {BMAmevt}  R Documentation 
Diagnostics for the MCMC output in the PB and NL models.
Description
The method issues several convergence diagnostics, in the particular case when the PB or the NL model is used. The code may be easily modified for other angular models.
Usage
diagnose(obj, ...)
## S3 method for class 'PBNLpostsample'
diagnose(
obj,
true.par = NULL,
from = NULL,
to = NULL,
autocor.max = 0.2,
default.thin = 50,
xlim.density = c(4, 4),
ylim.density = NULL,
plot = TRUE,
predictive = FALSE,
save = TRUE,
...
)
Arguments
obj 
an object of class 
... 
Additional parameters to be passed to the functions

true.par 
The true parameter. If 
from 
Integer or 
to 
Integer or 
autocor.max 
The maximum accepted autocorrelation for two successive parameters in the thinned sample. 
default.thin 
The default thinning interval if the above condition cannot be satisfied. 
xlim.density 
The 
ylim.density 
the 
plot 
Logical. Should plots be issued ? 
predictive 
Logical. Should the predictive density be plotted ? 
save 
Logical: should the result be saved ? Only used if the posterior sample has been saved itself (i.e. if it contains 
Value
A list made of
 predictive
The posterior predictive, or
0
ifpredictive=FALSE
 effective.size
the effective sample size of each component
 heidelTest
The first part of the Heidelberger and Welch test (stationarity test). The first row indicates “success” (1) or rejection(0), the second line shows the number of iterations to be discarded, the third line is the pvalue of the test statistic.
 gewekeTest
The test statistics from the Geweke stationarity test.
 gewekeScore
The pvalues for the above test statistics
 thin
The thinning interval retained
 correl.max.thin
The maximum autocorrelation for a lag equal to
thin
 linked.est.mean
The posterior mean of the transformed parameter (on the real line)
 linked.est.sd
The standard deviation of the transformed parameters
 est.mean
The posterior mean of the original parameters, as they appears in the expression of the likelihood
 sample.sd
the posterior standard deviation of the original parameters