R Dataset / Package MASS / beav2

On this R-data statistics page, you will find information about the beav2 data set which pertains to Body Temperature Series of Beaver 2. The beav2 data set is found in the MASS R package. You can load the beav2 data set in R by issuing the following command at the console data("beav2"). This will load the data into a variable called beav2. If R says the beav2 data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the beav2 R data set. The size of this file is about 1,693 bytes.

Body Temperature Series of Beaver 2

Description

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.

Usage

beav2

Format

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.

Source

P. S. Reynolds (1994) Time-series 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.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

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)

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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