# Example 8.22: latent class modeling using randomLCA

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In Example 8.21 we described how to fit a latent class model to data from the HELP dataset using SAS and R. Subjects were classified based on their observed (manifest) status on the following variables (on street or in shelter in past 180 days [homeless], CESD scores above 20, received substance abuse treatment [satreat], or linked to primary care [linkstatus]). We arbitrarily specify a three class solution.**SAS and R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

In this example, we fit the same model using the

`randomLCA()`function within the package of the same name.

**R**

We begin by reading in the data.

ds = read.csv("http://www.math.smith.edu/r/data/help.csv") attach(ds) library(randomLCA)

We start by creating a dichotomous variable with high scores on the CESD, and put this together as part of a dataframe to be given as input.

cesdcut = ifelse(cesd>20, 1, 0) smallds = na.omit(data.frame(homeless, cesdcut, satreat, linkstatus)) results = randomLCA(smallds, nclass=3, notrials=1000) summary(results)

This generates the following output:

Classes AIC BIC logLik 3 2092.968 2149.893 -1032.484 Class probabilities Class 1 Class 2 Class 3 0.07846 0.70534 0.21620 Outcome probabilities homeless cesdcut satreat linkstatus Class 1 9.465e-06 0.5786 1.000e+00 9.538e-06 Class 2 4.375e-01 0.8322 9.988e-06 4.145e-01 Class 3 7.297e-01 0.8846 1.000e+00 3.971e-01

The results are equivalent to the results from the prior example, though the scientific notation for the observed prevalences in each class are hard to read. Other objects are available from the returned value, though they are also not in an easily digestible form:

> names(results) [1] "fit" "nclass" "classp" "outcomep" [5] "se" "np" "nobs" "logLik" [9] "observed" "fitted" "deviance" "classprob" [13] "bics" "random" "level2" "byclass" [17] "blocksize" "call" "probit" "quadpoints" [21] "patterns" "notrials" "freq" > results$patterns homeless cesdcut satreat linkstatus 1 0 0 0 0 2 0 0 0 1 3 0 0 1 0 4 0 0 1 1 5 0 1 0 0 6 0 1 0 1 7 0 1 1 0 8 0 1 1 1 9 1 0 0 0 10 1 0 0 1 11 1 0 1 0 12 1 0 1 1 13 1 1 0 0 14 1 1 0 1 15 1 1 1 0 16 1 1 1 1 > results$freq [1] 17 10 16 1 82 62 33 9 15 9 4 4 64 45 37 23

We’ll address workarounds for these shortcomings in a future entry.

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