Calculates the entropy of a cross-classification table produced as a density estimate using a latent class model.

1 | ```
poLCA.entropy(lc)
``` |

`lc` |
A model object estimated using the |

Entropy is a measure of dispersion (or concentration) in a probability mass function. For multivariate categorical data it is calculated

*H = -∑_c p_c log(p_c)*

where *p_c* is the share of the probability in the *c*th cell of the cross-classification table. A fitted latent class model produces a smoothed density estimate of the underlying distribution of cell percentages in the multi-way table of the manifest variables. This function calculates the entropy of that estimated probability mass function.

A number taking a minumum value of 0 (representing complete concentration of probability on one cell) and a maximum value equal to the logarithm of the total number of cells in the fitted cross-classfication table (representing complete dispersion, or equal probability for outcomes across every cell).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data(carcinoma)
f <- cbind(A,B,C,D,E,F,G)~1
lca2 <- poLCA(f,carcinoma,nclass=2) # log-likelihood: -317.2568
lca3 <- poLCA(f,carcinoma,nclass=3) # log-likelihood: -293.705
lca4 <- poLCA(f,carcinoma,nclass=4,nrep=10,maxiter=5000) # log-likelihood: -289.2858
# Maximum entropy (if all cases equally dispersed)
log(prod(sapply(lca2$probs,ncol)))
# Sample entropy ("plug-in" estimator, or MLE)
p.hat <- lca2$predcell$observed/lca2$N
H.hat <- -sum(p.hat * log(p.hat))
H.hat # 2.42
# Entropy of fitted latent class models
poLCA.entropy(lca2)
poLCA.entropy(lca3)
poLCA.entropy(lca4)
``` |

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