hivtests {randomLCA} | R Documentation |
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.
data(hivtests)
A data frame with 16 observations on the following 5 variables.
V1
V2
V3
V4
freq
Qu, Tan and Kutner (1989)
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 teh 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.
data(hivtests) # fit 2LC model from Qu et al (1996) hivtests.lca <- randomLCA(hivtests[,1:4],freq=hivtests$freq) # fit model with random effect # this is different to teh Qu paper which only applies the RE to tests 2 and 3 hivtests.lcarandom <- randomLCA(hivtests[,1:4],freq=hivtests$freq, initmodel=hivtests.lca,random=TRUE,blocksize=1,probit=TRUE)