pls.regression.cv        package:plsgenomics        R Documentation

_D_e_t_e_r_m_i_n_a_t_i_o_n _o_f _t_h_e _n_u_m_b_e_r _o_f _l_a_t_e_n_t _c_o_m_p_o_n_e_n_t_s _t_o _b_e _u_s_e_d _i_n _P_L_S _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     The function 'pls.regression.cv' determines the best number of
     latent components to be used for PLS regression using the
     cross-validation approach described in Boulesteix and Strimmer
     (2005).

_U_s_a_g_e:

     pls.regression.cv(Xtrain, Ytrain, ncomp, nruncv=20, alpha=2/3)

_A_r_g_u_m_e_n_t_s:

  Xtrain: a (ntrain x p) data matrix containing the predictors for the
          training data set. 'Xtrain' may be a matrix or a data frame.
          Each row is an observation and each column is a  predictor
          variable.

  Ytrain: a (ntrain x m) data matrix of responses. 'Ytrain' may be a
          vector (if m=1), a matrix or a data frame.  If 'Ytrain' is a
          matrix or a data frame, each row is an observation and each
          column is a response variable. If 'Ytrain' is a vector, it
          contains the unique response variable for each observation.

   ncomp: the vector of integers from which the best number of latent 
          components has to be chosen by cross-validation. If 'ncomp'
          is of length 1, the best number of components is chosen from 
          1,...,'ncomp'.

  nruncv: the number of cross-validation iterations to be performed for
          the choice of the number of latent components.

   alpha: the proportion of observations to be included in the training
          set at each cross-validation iteration.

_D_e_t_a_i_l_s:

     The cross-validation procedure described in Boulesteix and
     Strimmer (2005)  is used to  determine the best number of latent
     components to be used for classification. At each cross-validation
     run, 'Xtrain' is split into a pseudo training set and a pseudo
     test set and the squared error is determined for each number of
     latent components. Finally, the function 'pls.regression.cv'
     returns the number of latent components for which the mean squared
     error over the 'nrun' partitions is minimal.

_V_a_l_u_e:

     The number of latent components to be used in PLS regression, as
     determined by cross-validation.

_A_u_t_h_o_r(_s):

     Anne-Laure Boulesteix (<URL:
     http://www.statistik.lmu.de/~socher/>)  and  Korbinian Strimmer
     (<URL: http://www.statistik.lmu.de/~strimmer/>).

_R_e_f_e_r_e_n_c_e_s:

     A. L. Boulesteix and K. Strimmer (2005). Predicting Transcription
     Factor Activities  from Combined Analysis of Microarray and ChIP
     Data: A Partial Least Squares Approach.

     S. de Jong (1993). SIMPLS: an alternative approach to partial
     least squares  regression, Chemometrics Intell. Lab. Syst. *18*,
     251-263.

_S_e_e _A_l_s_o:

     'pls.regression', 'TFA.estimate', 'pls.lda.cv'.

_E_x_a_m_p_l_e_s:

     # load plsgenomics library
     library(plsgenomics)

     # load Ecoli data
     data(Ecoli)

     # determine the best number of components for PLS regression using the cross-validation approach
     # choose the best number from 1,2,3,4
     pls.regression.cv(Xtrain=Ecoli$CONNECdata,Ytrain=Ecoli$GEdata,ncomp=4,nruncv=20)
     # choose the best number from 2,3
     pls.regression.cv(Xtrain=Ecoli$CONNECdata,Ytrain=Ecoli$GEdata,ncomp=c(2,3),nruncv=20)

