summary.bayesplfm | R Documentation |
The function summary.bayesplfm
summarizes the output of the object generated by the
bayesplfm
function.
## S3 method for class 'bayesplfm' summary(object, ...)
object |
Bayesian probabilistic latent feature analysis object returned by |
... |
Further arguments are ignored |
The summary of the Bayesian probabilistic latent feature analysis objects displays:
The parameters used to call the bayesplfm
function.
Information on the descriptive fit of the model (i.e. correlation between observed and expected frequencies, and proportion of the variance in the observed frequencies accounted for by the model).
The posterior mean of the object- and attribute parameters.
95 percent posterior intervals for the object- and attribute parameters.
Rhat convergence values for object- and attribute parameters (if Nchains
>1).
call |
Parameters used to call the function. |
descriptivefit |
A list with two measures of descriptive fit on the J X K table: (1) the correlation between observed and expected frequencies, and (2) the proportion of the variance in the observed frequencies accounted for by the model. |
objpar |
A J X F matrix with the posterior mean of the object parameters computed on all iterations and chains in the sample. |
attpar |
A K X F matrix with the posterior mean of the attribute parameters computed on all iterations and chains in the sample. |
p95objpar |
95 percent posterior intervals of object parameters. |
p95attpar |
95 percent posterior intervals of attribute parameters. |
Rhatobjpar |
Rhat convergence values for object parameters. |
Rhatattpar |
Rhat convergence values for attribute parameters. |
bayesplfm
## Not run: ##load car data data(car) ## compute 5 runs of disjunctive model with 2 features carem2<-plfm(maprule="disj",freq1=car$freq1,freqtot=car$freqtot,F=2,M=5) ## Compute a sample of the posterior distribution ## for the disjunctive model with two features ## use the posterior mode obtained with the previous plfm analysis carbayes2<-bayesplfm(maprule="disj",freq1=car$freq1,freqtot=car$freqtot,F=2, maxNiter=500,Nburnin=0,Nstep=100,Nchains=2, start.bayes="fitted.plfm",fitted.plfm=carem2) ## compute a summary of the object generated by bayesplfm summarycarbayes2<-summary(carbayes2) ## End(Not run)
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