uterinecarcinoma {randomLCA} | R Documentation |
Uterine Carcinoma Data
Description
Classification of 118 histology samples by 118 pathologists. Original classification in Holmquist et al (1967) was to one of five categories, this has been reduced to two. Analysed by a number of authors, with a random effects model in Qu et al (1996).
Usage
uterinecarcinoma
Format
A data frame with 20 observations on the following 8 variables.
V1
Pathologist 1
V2
Pathologist 2
V3
Pathologist 3
V4
Pathologist 4
V5
Pathologist 5
V6
Pathologist 6
V7
Pathologist 7
freq
Number of observed pattern
Source
Qu et al (1996)
References
Holmquist, N.D., McMahan, C.A., and Williams, O.D. (1967) Variability in classification of carcinoma in situ of the uterine cervix. Archives of Pathology, 84, 344–345.
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
uterinecarcinoma.lcarandom2 <- randomLCA(uterinecarcinoma[, 1:7],
freq = uterinecarcinoma$freq, random = TRUE, probit = TRUE, quadpoints = 61, cores = 1)
# LCR1 model of Que et al. This is fairly unstable and
# is also slow and doesn't improve the model fit
uterinecarcinoma.lcarandom2by <- randomLCA(uterinecarcinoma[, 1:7], freq = uterinecarcinoma$freq,
byclass = TRUE, random = TRUE, probit = TRUE, quadpoints = 71, cores = 1)