lars                  package:lars                  R Documentation

_F_i_t_s _L_e_a_s_t _A_n_g_l_e _R_e_g_r_e_s_s_i_o_n, _L_a_s_s_o _a_n_d _I_n_f_i_n_i_t_e_s_i_m_a_l _F_o_r_w_a_r_d _S_t_a_g_e_w_i_s_e _r_e_g_r_e_s_s_i_o_n _m_o_d_e_l_s

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

     These are all variants of Lasso, and provide the entire sequence
     of coefficients and fits, starting from zero, to the least squares
     fit.

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

     lars(x, y, type = c("lasso", "lar", "forward.stagewise"), 
         trace = FALSE, Gram, eps = .Machine$double.eps, max.steps, use.Gram = TRUE)

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

       x: matrix of predictors 

       y: response  

    type: One of  "lasso", "lar"  or "forward.stagewise". The names can
          be abbreviated to any unique substring. Default is "lasso". 

   trace: If TRUE, lars prints out its progress 

    Gram: The X'X matrix; useful for repeated runs (bootstrap) where a
          large X'X stays the same. 

     eps: An effective zero 

max.steps: Limit the number of steps taken; the default is '8 * min(m,
          n-1)', with m the number of variables, and n the number of
          samples. For 'type="lar"', the maximum number of steps is
          'min(m,n-1)'. For 'type="lasso"' and especially
          'type="forward.stagewise"', there can be many more terms,
          because although no more than 'min(m,n-1)' variables can be
          active during any step, variables are frequently droppped and
          added as the algorithm proceeds. Although the default usually
          guarantees that the algorithm has proceeded to the saturated
          fit, users should check. 

use.Gram: When the number m of variables is very large, i.e. larger
          than N, then you may not want LARS to precompute the Gram
          matrix. Default is use.Gram=TRUE 

_D_e_t_a_i_l_s:

     LARS is described in detail in Efron, Hastie, Johnstone and
     Tibshirani (2002). With the "lasso" option, it computes the
     complete lasso solution simultaneously for ALL values of the
     shrinkage parameter in the same computational cost as a least
     squares fit.

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

     A "lars" object is returned, for which print, plot and predict
     methods exist.

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

     Brad Efron and Trevor Hastie

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

     Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle
     Regression" (with discussion) _Annals of Statistics_; see also
     <URL:
     http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.p
     s>. Hastie, Tibshirani and Friedman (2002) Elements of Statistical
     Learning, Springer, NY.

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

     print, plot, and predict methods for lars, and  cv.lars

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

     data(diabetes)
     par(mfrow=c(2,2))
     attach(diabetes)
     object <- lars(x,y)
     plot(object)
     object2 <- lars(x,y,type="lar")
     plot(object2)
     object3 <- lars(x,y,type="for") # Can use abbreviations
     plot(object3)
     detach(diabetes)

