randomLCA (version 1.1-4)

hivtests: HIV testing data

Description

Serum samples are tested for HIV by 4 different biossays in Alvord et al (1988) and sensitivity and specificity determined using latent class analysis. Qu et al (1996) repeat the analysis using a model incorporating a random effect.

Usage

hivtests

Format

A data frame with 16 observations on the following 5 variables.

V1

Test 1

V2

Test 2

V3

Test 3

V4

Test 4

freq

Number of subjects

References

Alvord, W.G., Drummond, J.E., Arthur, L.O., Goedert, J.J., Levine, P.H., Murphy, E.L., Weiss, S.H., and Blattner, W.A. (1988) A method for predicting individual HIV infection status in the absence of clinical information. AIDS Research and Human Retroviruses, 4, 295--304.

Qu, Y., Tan, M. and Kutner, M.H. (1996) Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics, 52, 797--810.

Examples

# \donttest{
# fit standard latent class
hivtests.lca2 <- randomLCA(hivtests[, 1:4], freq = hivtests$freq, cores = 1)
# with random effect and constant loading
hivtests.lca2random <- randomLCA(hivtests[, 1:4], freq = hivtests$freq, random = TRUE,
    quadpoints = 101, penalty = 1.0, cores = 1)
# with random effect and variable loading
# for this model there are 13 parameters fitted to 16 observations, so model is fairly unstable
hivtests.lca2random2 <- randomLCA(hivtests[, 1:4], freq = hivtests$freq, random = TRUE, 
    constload = FALSE, quadpoints = 101, penalty = 1.0, cores = 1)
# BIC shows best model is random effects with constant loading
print(c(BIC(hivtests.lca2), BIC(hivtests.lca2random), BIC(hivtests.lca2random2)))# }