Posterior probabilities from a latent class model
Calculates the posterior probability that cases belong to each latent class.
poLCA.posterior(lc,y,x=NULL)
lc |
A model object estimated using the |
y |
A vector or matrix containing series of responses on the manifest variables in |
x |
An optional vector or matrix of covariate values, if |
From the parameters estimated by the latent class model, this function calculates the "posterior" probability that a specified case – characterized by values of the manifest variables y, and, if a latent class regression model, concomitant variables x – "belongs to" each latent class in lc. For observed cases, this information is also contained in the lc model object as lc$posterior. The added benefit of this function is that it can calculate posterior class membership probabilities for arbitrary values of x and y, whether or observed or not.
A matrix containing posterior probabilities corresponding to the specified sets of responses y, based on the estimated latent class model lc. For each row (one case), the first column gives the posterior probability of being in class 1, the second column gives the posterior probability of being in class 2, and so forth. Across rows, these probabilities sum to one.
data(election)
## Basic latent class model with three classes
f1 <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG,
MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~1
lc1 <- poLCA(f1,election,nclass=3) # log-likelihood: -16714.66
# The first observed case
lc1$y[1,]
lc1$posterior[1,]
poLCA.posterior(lc=lc1,y=as.numeric(lc1$y[1,]))
# A hypothetical case
poLCA.posterior(lc=lc1,y=rep(2,12))
# Entering y as a matrix
lc1$posterior[1:10,]
poLCA.posterior(lc=lc1,y=mapply(as.numeric,lc1$y[1:10,]))
## Latent class regression model with three classes
f2 <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG,
MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~AGE+EDUC+GENDER
lc2 <- poLCA(f2,election,nclass=3) # log-likelihood: -16598.38
# Posteriors for case number 97 (poorly classified)
lc2$y[97,]
lc2$x[97,]
lc2$posterior[97,]
poLCA.posterior(lc=lc2,y=as.numeric(lc2$y[97,]),x=c(41,6,1))
# If x is not specified, the posterior is calculated using the population average
poLCA.posterior(lc=lc2,y=as.numeric(lc2$y[97,]))
# Entering y and x as matrices
round(lc2$posterior[95:100,],2)
round(poLCA.posterior(lc=lc2,y=mapply(as.numeric,lc2$y[95:100,]),
x=as.matrix(lc2$x[95:100,-1])),2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.