Bayesian Latent Class Analysis

Description

Summary method for class "blca".

Usage

## S3 method for class 'blca'
summary(object, ...)

Arguments

Value

A brief summary consisting of two parts: the prior values specified to the model, and model diagnostics specific to the inference method used, such as information about the log-posterior (or lower bound in the case of blca.vb), as well the number of iterations the algorithm ran for, etc..

Author(s)

Arthur White

Examples

data(Alzheimer)
summary(blca.em(Alzheimer, 2))
summary(blca.vb(Alzheimer, 2, alpha=2, beta=2, delta=0.5))

## Not run: (fit.gibbs)<- blca.gibbs(Alzheimer, 2, delta=2)
## Not run: summary(fit.gibbs)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(BayesLCA)
Loading required package: e1071
Loading required package: coda
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BayesLCA/summary.blca.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.blca
> ### Title: Bayesian Latent Class Analysis
> ### Aliases: summary.blca summary.blca.boot summary.blca.em
> ###   summary.blca.gibbs summary.blca.vb print.summary.blca
> ### Keywords: summary blca
> 
> ### ** Examples
> 
> data(Alzheimer)
> summary(blca.em(Alzheimer, 2))
Restart number 1, logpost = -749.44... 
Restart number 2, logpost = -749.44... 
New maximum found... Restart number 3, logpost = -749.44... 
New maximum found... Restart number 4, logpost = -749.44... 
Restart number 5, logpost = -749.44... 
__________________

Bayes-LCA
Diagnostic Summary
__________________

Hyper-Parameters: 

 Item Probabilities:

 alpha: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             1        1          1         1       1         1
Group 2             1        1          1         1       1         1

 beta: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             1        1          1         1       1         1
Group 2             1        1          1         1       1         1

 Class Probabilities:

 delta: 
Group 1 Group 2 
      1       1 
__________________

Method: EM algorithm  

 Number of iterations: 56 

 Log-Posterior Increase at Convergence: 0.001142753 

 Log-Posterior: -749.436 

 AIC: -1524.872 

 BIC: -1570.12 
> summary(blca.vb(Alzheimer, 2, alpha=2, beta=2, delta=0.5))
Restart number 1, logpost = -1391.34... 
__________________

Bayes-LCA
Diagnostic Summary
__________________

Hyper-Parameters: 

 Item Probabilities:

 alpha: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             2        2          2         2       2         2
Group 2             2        2          2         2       2         2

 beta: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             2        2          2         2       2         2
Group 2             2        2          2         2       2         2

 Class Probabilities:

 delta: 
Group 1 Group 2 
    0.5     0.5 
__________________

Method: Variational Bayes  

 Number of iterations: 82 

 Lower Bound Increase at Convergence: 0.0001818211 

 Lower Bound: -1391.336 
> 
> ## Not run: (fit.gibbs)<- blca.gibbs(Alzheimer, 2, delta=2)
> ## Not run: summary(fit.gibbs)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>