Displaying 1 result from an estimated 1 matches for "p_5_angle".
2006 Nov 20
4
for help about logistic regression model
...5 -4.27189 27.7594 6.272780 1.205650 1.20123 1.633780
53.3304 41.98 57.68 0.1305950 91.1431
6 821p LEU 6 0.05675 27.5178 6.309750 1.370120 0.64664 1.656920
27.4681 0.00 0.00 0.0000000 94.0851
here p is random effect, and aa is nested in p
I do like this:
p5 <- read.csv("p_5_angle.csv", header=T, sep=",")
Y<-p5$sc>=90 # probability of pointing inward
library(MASS)
mp5.null <- glmmPQL(Y~1,data=p5,random=~1|p/aa,family=binomial(logit))
summary(mp5.null)
mp5.full<-glmmPQL(Y~as*ms*cur,data=p5,random=~1|p/aa,family=binomial(logit))
summary(mp5.full)...