missouri: Missouri lung cancer data

In npmlreg: Nonparametric Maximum Likelihood Estimation for Random Effect Models

Description

Lung cancer mortality in the 84 largest Missouri cities, for males aged 45-54, 1972-1981. Data presented in Tsutakawa (1985).

Usage

data(missouri)

Format

A data frame with 84 observations on the following 2 variables.

Size

population of the city.

Deaths

number of lung cancer deaths.

Details

The data set was analyzed using a Poisson model with normal random effect in Tsutakawa (1985), and using a binomial logit model with unspecified random effect distribution in Aitkin (1996b). Aitkin fitted this model with GLIM4.

Source

Tsutakawa, R. (1985).

References

Aitkin, M. (1996b). Empirical Bayes shrinkage using posterior random effect means from nonparametric maximum likelihood estimation in general random effect models. Statistical Modelling: Proceedings of the 11th IWSM 1996, 87-94.

Tsutakawa, R. (1985). Estimation of Cancer Mortalilty Rates: A Bayesian Analysis of Small Frequencies. Biometrics 41, 69-79.

Examples

data(missouri)
alldist(Deaths~1, offset=log(Size), random=~1, k=2,
   family=poisson(link='log'), data=missouri)

Example output

1 ..2 ..3 ..4 ..5 ..6 ..7 ..8 ..9 ..10 ..11 ..12 ..13 ..14 ..15 ..16 ..17 ..18 ..19 ..
EM algorithm met convergence criteria at iteration #  19 
Disparity trend plotted.
EM Trajectories plotted.

Call:  alldist(formula = Deaths ~ 1, random = ~1, family = poisson(link = "log"),      data = missouri, k = 2, offset = log(Size)) 

Coefficients:
 MASS1   MASS2  
-4.844  -4.232  

Random effect distribution - standard deviation:	   0.2207407 

Mixture proportions:
    MASS1      MASS2  
0.8461624  0.1538376  
-2 log L:	    355.3 

npmlreg documentation built on May 2, 2019, 9:31 a.m.