Fst                  package:Biodem                  R Documentation

_C_a_l_c_u_l_a_t_e_s _t_h_e _F_s_t _f_r_o_m _t_h_e _c_o_n_d_i_t_i_o_n_a_l _k_i_n_s_h_i_p _m_a_t_r_i_x

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

     Calculates the Fst from a conditional kinship matrix.

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

     Fst(rval, N)

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

    rval: is a conditional kinship matrix, normally obtained by the
          functions 'R' and 'rel.cond' in the Biodem library. 

       N: the vector of effective populations size, nominally obtained
          by dividing the total population size by three. Starting form
          surname data, effective population size coincides with the
          number of marriages 

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

     The use of the Fst function follows Harpending and Jenkins 1974,
     and Jorde 1982. It gives an estimate of Wright's Fst, which is a
     measure of between-subdivision genetic heterogeneity.

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

     Fst returns one numeric value.

_N_o_t_e:

     ...

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

     Federico C. F. Calboli f.calboli@imperial.ac.uk

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

     Harpending, H. C. and T. jenkins. 1974. !Kung population
     structure. In: J. F. Crow and C. F. Denniston (eds.), Genetic
     distance, pp 137-161. Plenum Press, NY.

     Jorde, L. B. 1982. The genetic structure of the Utah mormons:
     migration analysis. Human Biology 54(3): 583-597.

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

     # Swedlund data again...
     data(P); data(S); data(N)
     # starting with how many cycles to equilibrium
     x<-mal.eq(S,P,N)
     # calculation of phi
     phi<-mal.phi(S,P,N,x)
     # calcutaion of the conditional kinship matrix
     cond<-mal.cond(phi,N)
     # finally! we get the Fst value
     fst<-Fst(cond,N)
     fst

     # 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
     iso.matrix <- uri(tot)
     iso.matrix # an unbiased random isonymy matrix
     reg <- rri(tot)
     reg # a coefficient of unbiased Regional Random Isonymy
     kin.cond <- rel.cond(iso.matrix,reg)
     kin.cond # a conditional kinship matrix
     N <- colSums(tot) # effective population size
     fst<-Fst(kin.cond,N)
     fst

