Reynolds (1994) describes a small part of a study of the long-term temperature dynamics of beaver Castor canadensis in north-central Wisconsin. Body temperature was measured by telemetry every 10 minutes for four females, but data from a one period of less than a day for each of two animals is used there.
beav2 data frame has 100 rows and 4 columns.
This data frame contains the following columns:
- Day of observation (in days since the beginning of 1990), November 3--4.
Time of observation, in the form
- Measured body temperature in degrees Celsius.
- Indicator of activity outside the retreat.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
attach(beav2) beav2$hours <- 24*(day-307) + trunc(time/100) + (time%%100)/60 plot(beav2$hours, beav2$temp, type = "l", xlab = "time", ylab = "temperature", main = "Beaver 2") usr <- par("usr"); usr[3:4] <- c(-0.2, 8); par(usr = usr) lines(beav2$hours, beav2$activ, type = "s", lty = 2) temp <- ts(temp, start = 8+2/3, frequency = 6) activ <- ts(activ, start = 8+2/3, frequency = 6) acf(temp[activ == 0]); acf(temp[activ == 1]) # also look at PACFs ar(temp[activ == 0]); ar(temp[activ == 1]) arima(temp, order = c(1,0,0), xreg = activ) dreg <- cbind(sin = sin(2*pi*beav2$hours/24), cos = cos(2*pi*beav2$hours/24)) arima(temp, order = c(1,0,0), xreg = cbind(active=activ, dreg)) library(nlme) # for gls and corAR1 beav2.gls <- gls(temp ~ activ, data = beav2, corr = corAR1(0.8), method = "ML") summary(beav2.gls) summary(update(beav2.gls, subset = 6:100)) detach("beav2"); rm(temp, activ)