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small gamma values cause NaNs for poisson regression #1

@jamesdalg

Description

@jamesdalg
library(geepack)
data(seizure)
seiz.l <- reshape(seizure,
varying=list(c("base","y1", "y2", "y3", "y4")),
v.names="y", times=0:4, direction="long")
seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),]
seiz.l$t <- ifelse(seiz.l$time == 0, 8, 2)
seiz.l$x <- ifelse(seiz.l$time == 0, 0, 1)

m3 <- geeglm(y ~  x , id = id,
data=as.data.frame(subset(seiz.l,seiz.l$y<8&seiz.l$y!=0)), corstr="exch", family=poisson)
cvGEE::cv_gee(m3)
m3
m4 <- geeglm(y ~  x , id = id,
data=as.data.frame(seiz.l), corstr="exch", family=poisson)
cvGEE::cv_gee(m3)
cvGEE::cv_gee(m4)

m3

Call:
geeglm(formula = y ~ x, family = poisson, data = as.data.frame(subset(seiz.l,
seiz.l$y < 8 & seiz.l$y != 0)), id = id, corstr = "exch")

Coefficients:
(Intercept) x
2.0758 -0.7349

Degrees of Freedom: 145 Total (i.e. Null); 143 Residual

Scale Link: identity
Estimated Scale Parameters: [1] 0.7971

Correlation: Structure = exchangeable Link = identity
Estimated Correlation Parameters:
alpha
0.3115

Number of clusters: 48 Maximum cluster size: 5

m4

Call:
geeglm(formula = y ~ x, family = poisson, data = as.data.frame(seiz.l),
id = id, corstr = "exch")

Coefficients:
(Intercept) x
3.442 -1.329

Degrees of Freedom: 295 Total (i.e. Null); 293 Residual

Scale Link: identity
Estimated Scale Parameters: [1] 19.25

Correlation: Structure = exchangeable Link = identity
Estimated Correlation Parameters:
alpha
0.7739

Number of clusters: 59 Maximum cluster size: 5

This occurs because of the following lines in your package:

  counts <- object$family$linkinv(eta)
            scale <- object$geese$gamma
            shape_neg_bin <- (counts/(scale - 1))
            log_p_y <- dnbinom(x = resp, size = shape_neg_bin, 
                mu = counts, log = TRUE)

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