cpolyclass             package:polspline             R Documentation

_P_o_l_y_c_l_a_s_s: _p_o_l_y_c_h_o_t_o_m_o_u_s _r_e_g_r_e_s_s_i_o_n _a_n_d _m_u_l_t_i_p_l_e _c_l_a_s_s_i_f_i_c_a_t_i_o_n

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

     Classify new cases ('cpolyclass'), compute class probabilities for
     new cases ('ppolyclass'), and generate random multinomials for new
     cases ('rpolyclass') for a 'polyclass' model.

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

     cpolyclass(cov, fit)
     ppolyclass(data, cov, fit) 
     rpolyclass(n, cov, fit) 

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

     cov: covariates. Should be a matrix with 'fit\$ncov' columns.  
          For 'rpolyclass' 'cov' should either have one row, in  which
          case all random numbers are based on the same covariates, or
          'n'  rows in which case each random number has its own
          covariates.  

     fit: 'polyclass' object, typically the result of 'polyclass'. 

    data: there are several possibilities. If data is a vector with as
          many elements  as cov has rows, each element of data
          corresponds to a row of cov; if  only one value is given, the
          probability of being in that class is computed  for all sets
          of covariates. If data is omitted, all class probabilities
          are  provided.  

       n: number of pseudo random numbers to be generated.  

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

     Most likely classes ('cpolyclass'), probabilities ('cpolyclass'),
     or random classes according to the estimated probabilities
     ('rpolyclass').

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

     Charles Kooperberg clk@fhcrc.org.

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

     Charles Kooperberg, Smarajit Bose, and  Charles J. Stone (1997).
     Polychotomous regression. _Journal of the American Statistical
     Association_, *92*, 117-127.

     Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K.
     Truong. The use of polynomial splines and their tensor products in
     extended linear modeling (with discussion) (1997).  _Annals of
     Statistics_, *25*, 1371-1470.

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

     'polyclass', 'plot.polyclass',  'summary.polyclass',
     'beta.polyclass'.

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

     data(iris)
     fit.iris <- polyclass(iris[,5], iris[,1:4])
     class.iris <- cpolyclass(iris[,1:4], fit.iris)
     table(class.iris, iris[,5])
     prob.setosa <- ppolyclass(1, iris[,1:4], fit.iris)
     prob.correct <- ppolyclass(iris[,5], iris[,1:4], fit.iris) 
     rpolyclass(100, iris[64,1:4], fit.iris)

