3.3 Generalized estimating equations model

Below logistic model takes account of repeat observation by cow. Examine different correlation structures. It is of interest to note that (odds of) high protein content in milk appears to decrease as time since calving increases.

Milk <- Milk |> modify_if(is.character, as.factor);
Milk <- Milk |> tidyr::drop_na()
Milk <- droplevels(Milk);
Milk <- Milk |> dplyr::arrange(Yard, Cow, Time);

Milk <- Milk[order(Milk$Cow), ]; 
rm_mvsum(model=geeglm(HP ~ Diet + Yard + Time, id=c(Cow), data=Milk, family=binomial, corstr="ar1"), data=Milk, showN=T, CIwidth=0.95);

	OR(95%CI)	p-value	N (obs|clusters)	Event (total|clusters with event)	VIF
Diet			1337|79	386|72	1.05
barley	Reference		425|25	183|24
barley+lupins	0.46 (0.26, 0.81)	0.006	459|27	119|26
lupins	0.32 (0.18, 0.56)	<0.001	453|27	84|22
Yard			1337|79	386|72	1.03
1	Reference		188|12	56|10
2	1.20 (0.59, 2.43)	0.62	307|18	78|17
3	0.90 (0.42, 1.91)	0.78	263|15	63|12
4	1.07 (0.54, 2.13)	0.85	324|19	86|19
5	1.75 (0.85, 3.61)	0.13	255|15	103|14
Time	0.92 (0.89, 0.96)	<0.001	1337|79	386|72	1.05