poLCAParallel.goodnessfit

Finds unique responses and put them in a dataframe along with the observed
and expected frequencies. Also calculates the log likelihood ratio and chi
squared statistics.

A 'C++' reimplementation of 'poLCA' - latent class analysis and
latent class regression models for polytomous outcome variables, also known
as latent structure analysis. It attempts to reproduce results and be as
similar as possible to the original code, while running faster, especially
with multiple repetitions, by utilising multiple threads. Further reading is
available on the Queen Mary, University of London, IT Services Research blog
<https://blog.hpc.qmul.ac.uk/speeding_up_r_packages/>.

Sherman Lo

poLCAParallel

Polytomous Variable Latent Class Analysis Parallel

Drew Linzer

Jeffrey Lewis.

poLCAParallel.goodnessfit function

<dl><dt>results</dt>
<dd>A model object estimated using the <code>poLCA</code> function</dd></dl>

Arguments

Add predcell, Gsq and Chisq to a fitted poLCA object — poLCAParallel.goodnessfit

<dl>

<dt>results</dt>
<dd>A model object estimated using the <code>poLCA</code> function</dd>

</dl>

poLCAParallel.goodnessfit: Add predcell, Gsq and Chisq to a fitted poLCA object

Description

Usage

Value

Arguments

Details