crossval             package:pls             R Documentation(latin1)

_C_r_o_s_s-_v_a_l_i_d_a_t_i_o_n _o_f _P_L_S_R _a_n_d _P_C_R _m_o_d_e_l_s

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

     A "stand alone" cross-validation function for 'mvr' objects.

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

     crossval(object, segments = 10,
              segment.type = c("random", "consecutive", "interleaved"),
              length.seg, jackknife = FALSE, trace = 15, ...)

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

  object: an 'mvr' object; the regression to cross-validate.

segments: the number of segments to use, or a list with segments (see
          below).  Ignored if 'loo = TRUE'.

segment.type: the type of segments to use.  Ignored if 'segments' is a
          list.

length.seg: Positive integer.  The length of the segments to use.  If
          specified, it overrides 'segments' unless 'segments' is a
          list.

jackknife: logical.  Whether jackknifing of regression coefficients
          should be performed.

   trace: if 'TRUE', tracing is turned on.  If numeric, it denotes a
          time limit (in seconds).  If the estimated total time of the
          cross-validation exceeds this limit, tracing is turned on.

     ...: additional arguments, sent to the underlying fit function.

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

     This function performs cross-validation on a model fit by 'mvr'.
     It can handle models such as 'plsr(y ~ msc(X), ...)' or other
     models where the predictor variables need to be recalculated for
     each segment.  When recalculation is not needed, the result of
     'crossval(mvr(...))' is identical to 'mvr(..., validation =
     "CV")', but slower.

     Note that to use 'crossval', the data _must_ be specified with a
     'data' argument when fitting 'object'.

     If 'segments' is a list, the arguments 'segment.type' and
     'length.seg' are ignored.  The elements of the list should be
     integer vectors specifying the indices of the segments.  See
     'cvsegments' for details.

     Otherwise, segments of type 'segment.type' are generated.  How
     many segments to generate is selected by specifying the number of
     segments in 'segments', or giving the segment length in
     'length.seg'.  If both are specified, 'segments' is ignored.

     If 'jackknife' is 'TRUE', jackknifed regression coefficients are
     returned, which can be used for for variance estimation
     ('var.jack') or hypothesis testing ('jack.test').

     When tracing is turned on, the segment number is printed for each
     segment.

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

     The supplied 'object' is returned, with an additional component
     'validation', which is a list with components 

  method: euqals '"CV"' for cross-validation.

    pred: an array with the cross-validated predictions.

coefficients: (only if 'jackknife' is 'TRUE') an array with the
          jackknifed regression coefficients.  The dimensions
          correspond to the predictors, responses, number of
          components, and segments, respectively.

  PRESS0: a vector of PRESS values (one for each response variable) for
          a model with zero components, i.e., only the intercept.

   PRESS: a matrix of PRESS values for models with 1, ..., 'ncomp'
          components.  Each row corresponds to one response variable.

     adj: a matrix of adjustment values for calculating bias corrected
          MSEP.  'MSEP' uses this.

segments: the list of segments used in the cross-validation.

   ncomp: the number of components.

_N_o_t_e:

     The 'PRESS0' is always cross-validated using leave-one-out
     cross-validation.  This usually makes little difference in
     practice, but should be fixed for correctness.

     The current implementation of the jackknife stores all
     jackknife-replicates of the regression coefficients, which can be
     very costly for large matrices.  This might change in a future
     version.

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

     Ron Wehrens and Bjrn-Helge Mevik

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

     Mevik, B.-H., Cederkvist, H. R. (2004) Mean Squared Error of
     Prediction (MSEP) Estimates for Principal Component Regression
     (PCR) and Partial Least Squares Regression (PLSR). _Journal of
     Chemometrics_, *18*(9), 422-429.

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

     'mvr' 'mvrCv' 'cvsegments' 'MSEP' 'var.jack' 'jack.test'

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

     data(yarn)
     yarn.pcr <- pcr(density ~ msc(NIR), 6, data = yarn)
     yarn.cv <- crossval(yarn.pcr, segments = 10)
     ## Not run: plot(MSEP(yarn.cv))

