Pima.tr                 package:MASS                 R Documentation

_D_i_a_b_e_t_e_s _i_n _P_i_m_a _I_n_d_i_a_n _W_o_m_e_n

_D_e_s_c_r_i_p_t_i_o_n:

     A population of women who were at least 21 years old, of Pima
     Indian heritage and living near Phoenix, Arizona,  was tested for
     diabetes according to World Health Organization criteria.  The
     data were collected by the US National Institute of Diabetes and
     Digestive and Kidney Diseases. We used the 532 complete records
     after dropping the (mainly missing) data on serum insulin.

_U_s_a_g_e:

     data(Pima.tr)
     data(Pima.tr2)
     data(Pima.te)

_F_o_r_m_a_t:

     These data frames contains the following columns:

     '_n_p_r_e_g' number of pregnancies

     '_g_l_u' plasma glucose concentration in an oral glucose tolerance
          test

     '_b_p' diastolic blood pressure (mm Hg)

     '_s_k_i_n' triceps skin fold thickness (mm)

     '_b_m_i' body mass index (weight in kg/(height in m)^2)

     '_p_e_d' diabetes pedigree function

     '_a_g_e' age in years

     '_t_y_p_e' 'Yes' or 'No', for diabetic according to WHO criteria

_D_e_t_a_i_l_s:

     The training set 'Pima.tr' contains a randomly selected set of 200
     subjects, and 'Pima.te' contains the remaining 322 subjects.
     'Pima.tr2' contains 'Pima.tr' plus 100 subjects with missing
     values in the explanatory variables.

_S_o_u_r_c_e:

     Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C. and
     Johannes, R. S. (1988) Using the ADAP learning algorithm to
     forecast the onset of _diabetes mellitus_. In _Proceedings of the
     Symposium on Computer Applications in Medical Care (Washington,
     1988),_ ed. R. A. Greenes, pp. 261-265. Los Alamitos, CA: IEEE
     Computer Society Press.

     Ripley, B.D. (1996) _Pattern Recognition and Neural Networks._
     Cambridge: Cambridge University Press.

