R Graphical Manual

Last data update: 2014.03.03

Data Source

R Release (3.2.3)
CranContrib
BioConductor
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Data Type

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Functions
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Classification

Results 1 - 10 of 15 found.
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data.blca (Package: BayesLCA) :

Conveniently format data for use with blca.

● Data Source: CranContrib
● Keywords: blca, data.blca
● Alias: data.blca
● 0 images

Zscore (Package: BayesLCA) :

For a fitted model of class blca, and binary data X, the probability of class membership for each data point is provided.

● Data Source: CranContrib
● Keywords: blca
● Alias: Zscore, Zscore.internal
● 0 images

summary.blca (Package: BayesLCA) :

Summary method for class "blca".

● Data Source: CranContrib
● Keywords: blca, summary
● Alias: print.summary.blca, summary.blca, summary.blca.boot, summary.blca.em, summary.blca.gibbs, summary.blca.vb
● 0 images

Latent class analysis (LCA) attempts to find G hidden classes in binary data X. blca.em utilises an expectation-maximisation algorithm to find maximum a posteriori (map) estimates of the parameters.

● Data Source: CranContrib
● Keywords: blca, em
● Alias: blca.em
● 1 images

blca.vb (Package: BayesLCA) :

Latent class analysis (LCA) attempts to find G hidden classes in binary data X. blca.vb uses a variational EM algorithm to find the distribution which best approximates the parameters' true distribution.

● Data Source: CranContrib
● Keywords: blca, variational
● Alias: blca.vb
● 1 images

BayesLCA-package (Package: BayesLCA) :

Bayesian latent class analysis using several different methods.

● Data Source: CranContrib
● Keywords: package
● Alias: BayesLCA, BayesLCA-package
● 2 images

rlca (Package: BayesLCA) :

A function which randomly generates data with respect to some underlying latent class. Data may be generated either by specifying item and class probabilities or by utilising an object previously fitted to data.

● Data Source: CranContrib
● Keywords: blca, random
● Alias: rlca
● 0 images

blca.boot (Package: BayesLCA) :

Latent class analysis (LCA) attempts to find G hidden classes in binary data X. blca.boot repeatedly samples from X with replacement then utilises an EM algorithm to find maximum posterior (MAP) and standard error estimates of the parameters.

● Data Source: CranContrib
● Keywords: blca, bootstrap
● Alias: blca.boot
● 7 images

MAP (Package: BayesLCA) :

MAP obtains maximum a posteriori (MAP) classifications. unMAP converts a classification vector into an indicator matrix.

● Data Source: CranContrib
● Keywords: map, unmap
● Alias: MAP, unMAP
● 0 images

blca.gibbs (Package: BayesLCA) :

Latent class analysis (LCA) attempts to find G hidden classes in binary data X. blca.gibbs performs Gibbs sampling to sample from the parameters' true distribution.

● Data Source: CranContrib
● Keywords: blca, gibbs
● Alias: blca.gibbs
● 0 images