

beav2(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_:

     The `beav2' data frame has 100 rows and 4 columns on
     body temperature measurements at 10 minute intervals.

_A_r_g_u_m_e_n_t_s_:

     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 Celcius

   activ: Indicator of activity outside the retreat

_F_o_r_m_a_t_:

     Reynolds (1994) describes a small part of a study of
     the long-term temperature dynamics of beaver Castor
     canadensis in north-central Wisconsin.  Body tempera-
     ture 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.

     This data frame contains the following columns:

_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 Green-
     house, J. eds (1994) Case Studies in Biometry.  New
     York: John Wiley and Sons.

_S_e_e _A_l_s_o_:

     `beav1'

_E_x_a_m_p_l_e_s_:

     library(ts)
     data(beav2)
     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])

     arima0(temp, order=c(1,0,0))
     arima0(temp, order=c(1,0,0), xreg=activ)
     dreg <- cbind(sin=sin(2*pi*beav2$hours/24), cos=cos(2*pi*beav2$hours/24))
     arima0(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)

