summary.plfm {plfm} | R Documentation |
Summarizing probabilistic latent feature analysis
Description
The function summary.plfm
summarizes the main output of plfm
including estimates and standard errors for
object- and attribute parameters, model selection criteria, and goodness-of-fit measures.
Usage
## S3 method for class 'plfm'
summary(object, ...)
Arguments
object |
Probabilistic latent feature analysis object returned by |
... |
Further arguments are ignored |
Details
The summary of probabilistic latent feature analysis objects displays:
The parameters used to call the
plfm
function.The value of the loglikelihood, the deviance, the logarithm of the posterior density, the information criteria AIC and BIC.
The result of a Pearson chi-square goodness-of-fit test on the J X K table.
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 estimated object- and attribute parameters.
Asymptotic standard errors of the object- and attribute parameters.
Value
call |
Parameters used to call the function. |
informationcriteria |
List of information criteria that can be used for model selection. |
chisquaretest |
Pearson Chi-square test to evaluate the statistical goodness-of-fit of the model on the J X K object by attribute table of association frequencies. |
descriptivefit |
A list of measures to evaluate the descriptive goodness-of-fit of the model on the J X K object by attribute table of association frequencies. |
objpar |
A J X F matrix of estimated object parameters. |
SE.objpar |
A J X F matrix of estimated standard errors of object parameters. |
attpar |
A K X F matrix of estimated attribute parameters. |
SE.attpar |
A K X F matrix of estimated standard errors of attribute parameters |
Author(s)
Michel Meulders
See Also
plfm
, print.plfm
, print.summary.plfm
Examples
## Perceptual analysis of associations between car models and car attributes
##load car data
data(car)
##compute the disjunctive model with 4 features
carf4<-plfm(maprule="disj",freq1=car$freq1,freqtot=car$freqtot,F=4,M=1)
## display a summary of the results
summary(carf4)