beav2                  package:MASS                  R Documentation

_B_o_d_y _T_e_m_p_e_r_a_t_u_r_e _S_e_r_i_e_s _o_f _B_e_a_v_e_r _2

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     beav2

_F_o_r_m_a_t:

     The 'beav2' data frame has 100 rows and 4 columns. This data frame
     contains the following columns:

     '_d_a_y' Day of observation (in days since the beginning of 1990),
          November 3-4.

     '_t_i_m_e' Time of observation, in the form '0330' for 3.30am

     '_t_e_m_p' Measured body temperature in degrees Celcius

     '_a_c_t_i_v' Indicator of activity outside the retreat

_S_o_u_r_c_e:

     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.

_R_e_f_e_r_e_n_c_e_s:

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

_S_e_e _A_l_s_o:

     'beav1'

_E_x_a_m_p_l_e_s:

     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)
     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)

