bolus {cold} | R Documentation |
The dataset has the number of requests per interval in 12 successive four-hourly intervals following abdominal surgery for 65 patients in a clinical trial to compare two groups (bolus/lock-out combinations).
data("bolus")
A data frame with 780 observations on the following 4 variables.
id
identifies de number of the individual profile. This vector contains observations of 65 individual profiles.
group
a factor with levels 1mg
and 2mg
.
time
a numeric vector that identifies the number of the time points observed.
y
a numeric vector with the number of analgesic doses taken by hospital patients in 12 successive four-hourly intervals.
The group 2mg
has 30 patients and the group 1mg
has 35 patients.
Weiss, Robert E. (2005). Modeling Longitudinal Data. Springer
https://robweiss.faculty.biostat.ucla.edu/book-data-sets
Henderson, R. and Shimakura, S. (2003). A Serially Correlated Gamma Frailty Model for Longitudinal Count Data. Biometrika, vol. 90, No. 2, 355–366
data(bolus) ## change the reference class contrasts(bolus$group) bolus$group<-relevel(factor(bolus$group),ref="2mg") contrasts(bolus$group) ## Weiss, Robert E. (2005) pp 353-356, compare with Table 11.2 bol0R<- cold(y~time+group, random=~1,data=bolus, dependence="indR") summary (bol0R) ## reparametrization of time bolus$time1<-seq(-1.1,1.1,0.2) bol0R1<- cold(y~time1+group, random=~1,data=bolus, dependence="indR") summary (bol0R1) bol1R1<- cold(y~time1+group, random=~1, data=bolus, time="time1", dependence="AR1R", aggregate=group) summary (bol1R1) anova(bol0R1,bol1R1) plot(bol1R1,which=1,ylab="Bolus count")