hedrick                package:Biodem                R Documentation

_C_a_l_c_u_l_a_t_e_s _t_h_e _H_e_d_r_i_c_k _s_t_a_n_d_a_r_d_i_z_e_d _k_i_n_s_h_i_p _c_o_e_f_f_i_c_i_e_n_t

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

     ``hedrick''calculates the Hedrick standardized kinship coefficient
     starting from surname frequencies.

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

     hedrick(x)

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

       x: is a surname frequency table where the N rows correspond to
          the surnames present in the whole population and the M
          columns are the subpopulations 

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

     The use of ``hedrick'' could be problematic, because different
     people are likely to arrange isonymy data in different ways on
     their computers. We decided for a matrix format for the isonymy
     data; the function would originally accept data in a different
     format and then convert it internally, but this would be a problem
     for people with data arranged in a different format. In the end we
     decided to write a specific function, "sur.freq", to generate
     surname frequency tables directly from raw marriage data or
     marriage-like data (the most commonly used sources in
     bio-demographic studies). For other types of surname data, see the
     verbose explanation in the info for the dataset "surnames" so it
     would be clear for the user how ``hedrick'' works.

_V_a_l_u_e:

     Returns a square symmetric standardized kinship matrix.

_N_o_t_e:

     The Hedrick index was originally conceived as a measure of the
     probability of genotypic identity between (sub)populations and
     uses a standardization analogous to that employed when calculating
     a correlation coefficient. As a consequence, it is equal to 1 if
     measured on populations with identical surname distribution.

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

     Federico C. F. Calboli and Alessio Boattini
     alessio.boattini2@unibo.it

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

     Hedrick, P. W. 1971. A new approach to measuring genetic
     similarity. Evolution 25: 276-280. Weiss, V. 1980. Inbreeding and
     genetic distance between hierarchically structured populations
     measured by surname frequencies. Mankind Quarterly 21: 135-149

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

     'sur.freq' to generate the input surname frequency table from
     marriage data, 'surnames' for an explanation on how to generate
     the correct input table from other surname sources, 'lasker'and
     'uri' for other types of inter-population kinship matrices

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

     # starting from a raw marriage records dataset:
     data(valley)
     tot <- sur.freq(valley,PAR,SURM,SURF)
     tot # a frequency table calculated above all the surnames
     hed.kin <- hedrick(tot)
     hed.kin # a standardized kinship matrix

     #starting from a generic surname frequency table
     data(surnames)
     surnames #a made-up dataset
     # you can see that the surnames are arranged as the _rows_ and
     # the populations are the _columns_
     # the use of the function "hedrick" just turns this data into a kinship matrix
     hed.kin <- hedrick(surnames)
     hed.kin

