The Glucose data frame has 196 rows and 4 columns. The dataset is originally in package nlme as Glucose2.
This data frame contains the following columns:
a factor with levels
1 to 7 identifying the subject whose glucose
level is measured.
a factor with levels
1
2
indicating the occasion in which the experiment was conducted.
a numeric vector giving the time since alcohol ingestion (in min/10).
a numeric vector giving the blood glucose level (in mg/dl) adjusted for baseline.
Hand and Crowder (Table A.14, pp. 180-181, 1996) describe data on
the blood glucose levels measured at 14 time points over 5 hours for 7
volunteers who took alcohol at time 0. The same experiment was
repeated on a second date with the same subjects but with a dietary
additive used for all subjects.
Dataset was corrected for baseline using the following code:
## dataset Glucose2 of package nlme
require(nlme)
Glucose2 <- Glucose2[order(Glucose2$Subject, Glucose2$Date, Glucose2$Time),]
## adjust for pre-infusion levels measured at time points -1 and 0
data <- NULL
for(i in unique(Glucose2$Subject)){
for(j in unique(Glucose2$Date)){
temp <- subset(Glucose2, Subject==i & Date==j)
temp$Conc <- temp$glucose - mean(c(temp$glucose[1], temp$glucose[2]))
temp$Conc <- ifelse(temp$Conc < 0 | temp$Time <= 0, 0, temp$Conc)
## handle intermediate values > 0
index1 <- which.max(temp$Conc)
index2 <- which.min(temp$Conc[-c(1:index1)]) + index1
if(temp$Conc[index2]==0){temp$Conc[c(index2:nrow(temp))] <- 0}
data <- rbind(data,temp)
}
}
Glucose <- subset(data, Time >= 0,
select=c('Subject', 'Date', 'Time', 'Conc'))
names(Glucose) <- c("id","date","time","conc")