rbvole               package:Rcapture               R Documentation

_R_o_b_u_s_t _D_e_s_i_g_n _D_a_t_a _f_o_r _R_e_d-_B_a_c_k _V_o_l_e_s

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

     This data set contains robust design capture history data for
     red-back voles.

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

     data(rbvole)

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

     '_c_1_1', '_c_1_2', '_c_1_3' Capture histories for the three capture
          occasions within primary period 1

     '_c_2_1', '_c_2_2', '_c_2_3' Capture histories for the three capture
          occasions within primary period 2

     '_c_3_1', '_c_3_2', '_c_3_3' Capture histories for the three capture
          occasions within primary period 3

     '_c_4_1', '_c_4_2', '_c_4_3' Capture histories for the three capture
          occasions within primary period 4

     '_c_5_1', '_c_5_2', '_c_5_3' Capture histories for the three capture
          occasions within primary period 5

     '_c_6_1', '_c_6_2', '_c_6_3' Capture histories for the three capture
          occasions within primary period 6

     _f_r_e_q Observed frequencies for each capture history

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

     Data collection was carried out by Etcheverry and al.. The capture
     occasions represent three consecutive days of trapping in May
     1999, July 1999, August 1999, May 2000, July 2000 and August 2000
     in the Duchenier conservation area in southeastern Quebec. This
     data set is analysed in Rivest and Daigle (2004).

     This data set's format is the alternative one, i.e. each row
     represents an observed capture history followed by its frequency.

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

     Rivest, L.P. and Daigle, G. (2004) Loglinear models for the robust
     design in mark-recapture experiments. _Biometrics_, *60*, 100-107.

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

       # According to Rivest and Daigle (2004), a good robust design model
       # for this data set is formed of an Mth Chao model for each period.
       # This model can be fitted as follows.
       
     ### data(rbvole)
     ### memory.limit(size=2000)
     ### rd<-robustd.t(rbvole,dfreq=TRUE,vt=rep(3,6),vm="Mth",vh="Chao")

       # WARNING : Because the data has 18 capture occasions, the fitting
       # of this model is very memory consuming; it is long to run!

