Data                package:wombsoft                R Documentation

_D_a_t_a _p_r_e_p_a_r_a_t_i_o_n

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

     Read a text file with coordinates and markers in columns and
     individuals in rows.

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

     DataDominant(input_file,conversion,nb_x,nb_y,output_coords="coord_km.txt")
     DataCodominant(input_file,conversion,nb_x,nb_y,output_coords="coord_km.txt")
     DataContingency(input_file,conversion,nb_x,nb_y,output_coords="coord_km.txt")

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

input_file: Path of the input text file. For dominant or codominant
          data, each row contains the name of the individual, the two
          coordinates (either abscissa and ordinates, or longitude and
          latitude), and the genetic data in succession. For
          contingency table, each row corresponds to a sampled point,
          with the name of the point, its coordinates, and the number
          of individuals for each modality of each variable.

conversion: 0 if the coordinates are cartesians, 1 if they are in
          degree and therefore need to be converted to cartesians.

nb_x,nb_y: number of pixels in width and length of the grid.

output_coords: the name of the file where the kilometer coordinates
          will be saved in. Default value is "coord_indiv.txt".

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

     a list of six items : 

spatial coordinates of individuals: a matrix with one line per
          individual, and two columns containing abscissa and ordinates
          of individuals, (x,y).

genetic_encoded: the genetic data, containing one column per locus. If
          data are dominant, it's the same table as the input file.  

    grid: a list of the vector of x, and the vector of y.

cvx_vertices: the       vertices of the convex hull of sampling area
          (same format than individuals coordinates).

cvx_matrix: a matrix containing a 1 if the corresponding point of the
          grid is in the convex hull, and a 0 otherwise.

nb_individual: the number of individuals in the dataset.

