glpls1a                 package:gpls                 R Documentation

_F_i_t _I_R_W_P_L_S _a_n_d _I_R_W_P_L_S_F _m_o_d_e_l

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

     Fit Iteratively ReWeighted Least Squares (IRWPLS) with an option
     of Firth's bias reduction procedure (IRWPLSF) for two-group
     classification

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

     glpls1a(X, y, K.prov = NULL, eps = 0.001, lmax = 100, b.ini = NULL, 
           denom.eps = 1e-20, family = "binomial", link = NULL, br = TRUE)

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

       X: n by p design matrix (with no intercept term)

       y: response vector 0 or 1

  K.prov: number of PLS components, default is the rank of X

     eps: tolerance for convergence

    lmax: maximum number of iteration allowed 

   b.ini: initial value of regression coefficients

denom.eps: small quanitity to guarantee nonzero denominator in deciding
          convergence

  family: glm family, 'binomial' is the only relevant one here 

    link: link function, 'logit' is the only one practically
          implemented now

      br: TRUE if Firth's bias reduction procedure is used

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

coefficients : regression coefficients

convergence : whether convergence is achieved

   niter: total number of iterations

bias.reduction: whether Firth's procedure is used

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

     Beiying Ding, Robert Gentleman

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


        *  Ding, B.Y. and Gentleman, R. (2003) _Classification using
           generalized partial least squares_. 

        *  Marx, B.D (1996) Iteratively reweighted partial least
           squares estimation for generalized linear regression.
           _Technometrics_ 38(4): 374-381.  

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

     'glpls1a.mlogit', 'glpls1a.logit.all', 'glpls1a.train.test.error',
     'glpls1a.cv.error', 'glpls1a.mlogit.cv.error'

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

      x <- matrix(rnorm(20),ncol=2)
      y <- sample(0:1,10,TRUE)
      ## no bias reduction
      glpls1a(x,y,br=FALSE)
       
      ## no bias reduction and 1 PLS component
      glpls1a(x,y,K.prov=1,br=FALSE)

      ## bias reduction
      glpls1a(x,y,br=TRUE)

