calcMargProb
(Package: randomLCA) :
Calculates Marginal Outcome Probabilities
Calculates the marginal outcome probabilities for a random effects latent class model, by integrating the outcome probability over the random effect. This is performed using Gauss-Hermite quadrature with the number of quadrature points specified for the model fitting.
● Data Source:
CranContrib
● Keywords: methods
● Alias: calcMargProb
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Summarises the fit of a randomLCA object.
● Data Source:
CranContrib
● Keywords: methods
● Alias: summary.randomLCA
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calcCond2Prob
(Package: randomLCA) :
Calculate Conditional Outcome Probabilities for 2 Level Models
The conditional probabilities are obtained integrating over the period random effect.
● Data Source:
CranContrib
● Keywords: methods
● Alias: calcCond2Prob
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refit
(Package: randomLCA) :
Refit an randomLCA object
Refits an randomLCA object using new data. For an example, see the simulate method.
● Data Source:
CranContrib
● Keywords: methods
● Alias: refit, refit.randomLCA
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BIC
(Package: randomLCA) :
BIC for randomLCA object
Returns BIC for a randomLCA object.
● Data Source:
CranContrib
● Keywords: methods
● Alias: BIC.randomLCA
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Extract fitted values for randomLCA object.
● Data Source:
CranContrib
● Keywords: methods
● Alias: fitted.randomLCA
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The posterior class probabililities for each observed pattern and class is determined. These are returned as a data frame together with the patterns for each observation. If class=0 is requested then all classes are returned, otherwise only the selected class.
● Data Source:
CranContrib
● Keywords: methods
● Alias: postClassProbs
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The class probabililities for the model are returned.
● Data Source:
CranContrib
● Keywords: methods
● Alias: classProbs
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AIC3
(Package: randomLCA) :
AIC with 3 penalty for randomLCA object
Returns AIC with penalty 3 for a randomLCA object.
● Data Source:
CranContrib
● Keywords: methods
● Alias: AIC3
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plot
(Package: randomLCA) :
Plot a randomLCA object
Plots the outcome probabilities for a randomLCA object, for random effects objects this can be either marginal or conditional or both. For a 2 level random effects model conditional2 will condition on the subject random effect and integrate over the period random effects. Note that plot is based on the xyplot function.
● Data Source:
CranContrib
● Keywords: methods
● Alias: plot.randomLCA
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providing 
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