capthist                package:secr                R Documentation

_S_p_a_t_i_a_l _C_a_p_t_u_r_e _H_i_s_t_o_r_y _O_b_j_e_c_t

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

     A 'capthist' object encapsulates all data needed by 'secr.fit',
     except for the optional habitat mask.

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

     An object of class 'capthist' holds spatial capture histories,
     detector (trap) locations, individual covariates and other data
     needed for a spatially explicit capture-recapture analysis with
     'secr.fit'. 

     For 'single' and 'multi' detectors, 'capthist' is a matrix with
     one row per animal and one column per occasion (i.e. dim(capthist)
     = c(nc, noccasions)); each element is either zero (no detection)
     or a detector number. For 'proximity' detectors, 'capthist' is an
     array of values in{} {-1, 0, 1} and dim(capthist) = c(nc,
     noccasions, ntraps). 

     Deaths during the experiment are represented as negative values. 

     Ancillary data are retained as attributes of a 'capthist' object
     as follows:

   _t_r_a_p_s object of class 'traps' (required)

   _s_e_s_s_i_o_n session identifier (required)

   _c_o_v_a_r_i_a_t_e_s dataframe of individual covariates (optional)

     The parts of a capthist object can be assembled with the function
     'make.capthist'.  Use 'sim.capthist' for Monte Carlo simulation
     (simple models only). Methods are provided to display and
     manipulate 'capthist' objects (print, summary, plot, rbind,
     subset, reduce) and to extract and replace attributes (covariates,
     traps).

     A multi-session 'capthist' object is a list in which each
     component is a 'capthist' for a single session.  The list maybe
     derived directly from multi-session input in Density format, or by
     combining existing 'capthist' objects with 'MS.capthist'.

_A_u_t_h_o_r(_s):

     Murray Efford murray.efford@otago.ac.nz

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

     Borchers, D. L. and Efford, M. G. (2008) Spatially explicit
     maximum likelihood methods for capture-recapture studies.
     _Biometrics_ *64*, 377-385.

     Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density
     estimation by spatially explicit capture-recapture:
     likelihood-based methods. In: D. L. Thomson, E. G. Cooch and M. J.
     Conroy (eds) _Modeling Demographic Processes in Marked
     Populations_. Springer, New York. Pp. 255-269.

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

     'traps', 'secr.fit', 'make.capthist', 'sim.capthist',
     'subset.capthist', 'rbind.capthist', 'MS.capthist',
     'reduce.capthist', 'mask'

