Description Usage Format Source References See Also Examples
Reynolds (1994) describes a small part of a study of the longterm temperature dynamics of beaver Castor canadensis in northcentral 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.
1 
The beav2
data frame has 100 rows and 4 columns.
This data frame contains the following columns:
day
Day of observation (in days since the beginning of 1990), November 3–4.
time
Time of observation, in the form 0330
for 3.30am.
temp
Measured body temperature in degrees Celsius.
activ
Indicator of activity outside the retreat.
P. S. Reynolds (1994) Timeseries analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. eds (1994) Case Studies in Biometry. New York: John Wiley and Sons.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  attach(beav2)
beav2$hours < 24*(day307) + trunc(time/100) + (time%%100)/60
plot(beav2$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))
## IGNORE_RDIFF_BEGIN
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)
## IGNORE_RDIFF_END

Call:
ar(x = temp[activ == 0])
Coefficients:
1
0.7392
Order selected 1 sigma^2 estimated as 0.02011
Call:
ar(x = temp[activ == 1])
Coefficients:
1
0.7894
Order selected 1 sigma^2 estimated as 0.01792
Call:
arima(x = temp, order = c(1, 0, 0), xreg = activ)
Coefficients:
ar1 intercept activ
0.8733 37.1920 0.6139
s.e. 0.0684 0.1187 0.1381
sigma^2 estimated as 0.01518: log likelihood = 66.78, aic = 125.55
Call:
arima(x = temp, order = c(1, 0, 0), xreg = cbind(active = activ, dreg))
Coefficients:
ar1 intercept active dreg.sin dreg.cos
0.7905 37.1674 0.5322 0.282 0.1201
s.e. 0.0681 0.0939 0.1282 0.105 0.0997
sigma^2 estimated as 0.01434: log likelihood = 69.83, aic = 127.67
Generalized least squares fit by maximum likelihood
Model: temp ~ activ
Data: beav2
AIC BIC logLik
125.5505 115.1298 66.77523
Correlation Structure: AR(1)
Formula: ~1
Parameter estimate(s):
Phi
0.8731771
Coefficients:
Value Std.Error tvalue pvalue
(Intercept) 37.19195 0.1131328 328.7460 0
activ 0.61418 0.1087286 5.6487 0
Correlation:
(Intr)
activ 0.582
Standardized residuals:
Min Q1 Med Q3 Max
2.42080776 0.61510520 0.03573836 0.81641138 2.15153496
Residual standard error: 0.2527856
Degrees of freedom: 100 total; 98 residual
Generalized least squares fit by maximum likelihood
Model: temp ~ activ
Data: beav2
Subset: 6:100
AIC BIC logLik
124.981 114.7654 66.49048
Correlation Structure: AR(1)
Formula: ~1
Parameter estimate(s):
Phi
0.8380448
Coefficients:
Value Std.Error tvalue pvalue
(Intercept) 37.25001 0.09634047 386.6496 0
activ 0.60277 0.09931904 6.0690 0
Correlation:
(Intr)
activ 0.657
Standardized residuals:
Min Q1 Med Q3 Max
2.0231494 0.8910348 0.1497564 0.7640939 2.2719468
Residual standard error: 0.2188542
Degrees of freedom: 95 total; 93 residual
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