yanomama                package:ade4                R Documentation

_D_i_s_t_a_n_c_e _M_a_t_r_i_c_e_s

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

     This data set gives 3 matrices about geographical, genetic and
     anthropometric distances.

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

     data(yanomama)

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

     'yanomama' is a list of 3 components:

     _g_e_o is a matrix of 19-19 geographical distances

     _g_e_n is a matrix of 19-19 SFA (genetic) distances

     _a_n_t is a matrix of 19-19 anthropometric distances

_S_o_u_r_c_e:

     Spielman, R.S. (1973)  Differences among Yanomama Indian villages:
     do the patterns of allele frequencies, anthropometrics and map
     locations correspond?  _American Journal of Physical
     Anthropology_, *39*, 461-480.

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

     Table 7.2 Distance matrices for 19 villages of Yanomama Indians. 
     All distances are as given by Spielman (1973), multiplied by 100
     for convenience in:  Manly, B.F.J. (1991)  _Randomization and
     Monte Carlo methods in biology_  Chapman and Hall, London, 1-281.

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

     library(mva)
         data(yanomama)
         gen <- quasieuclid(as.dist(yanomama$gen)) # depends of mva
         ant <- quasieuclid(as.dist(yanomama$ant)) # depends of mva
         par(mfrow = c(2,2))
         plot(gen, ant)
         t1 <- mantel.randtest(gen, ant, 99);
         plot(t1, main = "gen-ant-mantel") ; print(t1)
         t1 <- procuste.rtest(pcoscaled(gen), pcoscaled(ant), 99)
         plot(t1, main = "gen-ant-procuste") ; print(t1)
         t1 <- RV.rtest(pcoscaled(gen), pcoscaled(ant), 99)
         plot(t1, main = "gen-ant-RV") ; print(t1)

