uterinecarcinoma: Uterine Carcinoma Data

In randomLCA: Random Effects Latent Class Analysis

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)

randomLCA documentation built on July 9, 2023, 6:09 p.m.