Please don't e-mail me personally: please e-mail the r-sig-mixed-models help list ... It doesn't make sense to add overdispersion to a linear mixed model (i.e. one with normally distributed responses). See http://article.gmane.org/gmane.comp.lang.r.lme4.devel/6426 for example ...
On Sat, Aug 6, 2011 at 11:53 AM, Ahmad Rabiee <ahmadr at sbscibus.com.au> wrote:
Dear Ben
I?ve got a dataset (see attached) and I would like to run a mixed model
logistic regression. I would like to account for overdispersion in this
dataset.
I checked the examples in ?lme4 package manual?, but could understand how
you did this? Below is my R codes, I would appreciate if you can help, what
I need to do here to account for overdispersion in the following models?
Your help and comments would be greatly appreciated.
Ahmad
#--------------------------------------------------
library(lme4)
setwd("G:/Data")
ket <- read.table("z-score BHB data.csv", header=T, sep=",", na.string="NA")
# Model 1: Country as random effects term
ket.1a <- lmer(ketosis ~ z_bhb + bcs_pre + lact + twins + (1|country), ket)
# Model 2: herdno & Country as random effects terms
ket.1b <- lmer(ketosis ~ z_bhb + bcs_pre + lact + twins + (1|herdno) +
(1|country), ket)
ket.1b
# Model 3: herdno nested within country
ket.1c <- lmer(ketosis ~ z_bhb + bcs_pre + lact + twins + (herdno|country),
ket)
ket.1c
"Try not to become a man of success, but rather try to become a man of
value"
Albert Einstein
Please note my new email address is mailto:ahmadr at sbscibus.com.au. Please
update your records.