hare                package:Rcapture                R Documentation

_C_l_o_s_e_d _P_o_p_u_l_a_t_i_o_n _D_a_t_a _f_o_r _S_n_o_w_s_h_o_e _H_a_r_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This data set contains closed population capture history data for
     snowshoe hares.

_U_s_a_g_e:

     data(hare)

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

     '_c_1', '_c_2', '_c_3', '_c_4', '_c_5', '_c_6' Capture histories for the six
          capture occasions

_D_e_t_a_i_l_s:

     This data set is analysed in Cormack (1989) and Agresti (1994).

     This data set's format is the default one, i.e. each row
     represents the capture history of one animal.

_R_e_f_e_r_e_n_c_e_s:

     Agresti, A.  (1994). Simple capture-recapture models permitting
     unequal catchability and variable sampling effort. _Biometrics_,
     50, 494-500.

     Baillargeon, S. and Rivest, L.P. (2007). The Rcapture package:
     Loglinear models for capture-recapture in R. _Journal of
     Statistical Software_, to appear (available online at <URL:
     http://www.jstatsoft.org/>).

     Cormack, R. M. (1989). Log-linear models for capture-recapture.
     _Biometrics_, *45*, 395-413.

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

     data(hare)
     desc<-descriptive(hare)
     plot(desc)
       # The fi plot shows that the two animals caught on all occasions create 
       # some heterogeneity in the capture probabilities.

     closedp(hare)
       # The best fitting model Mth Poisson2(N = 81.1, s.e.=5.7) has an AIC of 146. 

       # One can compare the fit of this model with that obtained by removing the
       # 2 hares caught 6 times. This can be done by adding a column to the design 
       # matrix for Mt taking the value 1 for the capture history (1,1,1,1,1,1).
     col<-rep(0,2^6-1)
     mat<-histpos.t(6)
     col[apply(mat,1,sum)==6]<-1
     closedp.mX(hare,mX=cbind(mat,col),mname="Mt without 111111")
       # This gives N = 76.8 (s.e.=3.9) with an AIC of 146.

       # Profile likelihood confidence intervals for both models:
     profileCI(hare,m="Mth",h="Poisson",a=2)
     profileCI(hare,mX=cbind(mat,col),mname="Mt without 111111")

