mvrVal              package:pls              R Documentation(latin1)

_M_S_E_P, _R_M_S_E_P _a_n_d _R_2 _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:

     Functions to estimate the mean squared error of prediction (MSEP),
     root mean squared error of prediction (RMSEP) and R^2 (A.K.A.
     coefficient of multiple determination) for fitted PCR and PLSR
     models.  Test-set, cross-validation and calibration-set estimates
     are implemented.

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

     MSEP(object, ...)
     ## S3 method for class 'mvr':
     MSEP(object, estimate, newdata, ncomp = 1:object$ncomp, comps,
          intercept = cumulative, se = FALSE, ...)

     RMSEP(object, ...)
     ## S3 method for class 'mvr':
     RMSEP(object, ...)

     R2(object, estimate, newdata, ncomp = 1:object$ncomp, comps,
        intercept = cumulative, se = FALSE, ...)

     mvrValstats(object, estimate, newdata, ncomp = 1:object$ncomp, comps,
                 intercept = cumulative, se = FALSE, ...)

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

  object: an 'mvr' object

estimate: a character vector.  Which estimators to use. Should be a
          subset of 'c("all", "train", "CV", "adjCV", "test")'. 
          '"adjCV"' is only available for (R)MSEP.  See below for how
          the estimators are chosen.

 newdata: a data frame with test set data.

ncomp, comps: a vector of positive integers.  The components or number
          of components to use.  See below.

intercept: logical.  Whether estimates for a model with zero components
          should be returned as well.

      se: logical.  Whether estimated standard errors of the estimates
          should be calculated.  Not implemented yet.

     ...: further arguments sent to underlying functions or (for
          'RMSEP') to 'MSEP'

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

     'RMSEP' simply calls 'MSEP' and takes the square root of the
     estimates.  It therefore accepts the same arguments as 'MSEP'.

     Several estimators can be used.  '"train"' is the training or
     calibration data estimate, also called (R)MSEC.  For 'R2', this is
     the unadjusted R^2.  It is overoptimistic and should not be used
     for assessing models. '"CV"' is the cross-validation estimate, and
     '"adjCV"' (for 'RMSEP' and 'MSEP') is the bias-corrected
     cross-validation estimate.  They can only be calculated if the
     model has been cross-validated. Finally, '"test"' is the test set
     estimate, using 'newdata' as test set.

     Which estimators to use is decided as follows (see below for
     'mvrValstats').  If 'estimate' is not specified, the test set
     estimate is returned if 'newdata' is specified, otherwise the CV
     and adjusted CV (for 'RMSEP' and 'MSEP') estimates if the model
     has been cross-validated, otherwise the training data estimate. 
     If 'estimate' is '"all"', all possible estimates are calculated. 
     Otherwise, the specified estimates are calculated.

     Several model sizes can also be specified.  If 'comps' is missing
     (or is 'NULL'), 'length(ncomp)' models are used, with 'ncomp[1]'
     components, ..., 'ncomp[length(ncomp)]' components.  Otherwise, a
     single model with the components 'comps[1]', ...,
     'comps[length(comps)]' is used. If 'intercept' is 'TRUE', a model
     with zero components is also used (in addition to the above).

     The R^2 values returned by '"R2"' are calculated as 1 - SSE/SST,
     where SST is the (corrected) total sum of squares of the response,
     and SSE is the sum of squared errors for either the fitted values
     (i.e., the residual sum of squares), test set predictions or
     cross-validated predictions (i.e., the PRESS). For 'estimate =
     "train"', this is equivalent to the squared correlation between
     the fitted values and the response.  For 'estimate = "train"', the
     estimate is often called the prediction R^2.

     'mvrValstats' is a utility function that calculates the statistics
     needed by 'MSEP' and 'R2'.  It is not intended to be used
     interactively.  It accepts the same arguments as 'MSEP' and 'R2'. 
     However, the 'estimate' argument must be specified explicitly: no
     partial matching and no automatic choice is made.  The function
     simply calculates the types of estimates it knows, and leaves the
     other untouched.

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

     'mvrValstats' returns a list with components

     _S_S_E three-dimensional array of SSE values.  The first dimension is
          the different estimators, the second is the response
          variables and the third is the models.

     _S_S_T matrix of SST values.  The first dimension is the different
          estimators and the second is the response variables.

     _n_o_b_j a numeric vector giving the number of objects used for each
          estimator.

     _c_o_m_p_s the components specified, with '0' prepended if 'intercept'
          is 'TRUE'.

     _c_u_m_u_l_a_t_i_v_e 'TRUE' if 'comps' was 'NULL' or not specified.

     The other functions return an object of class '"mvrVal"', with
     components

     _v_a_l three-dimensional array of estimates.  The first dimension is
          the different estimators, the second is the response
          variables and the third is the models.

     _t_y_p_e '"MSEP"', '"RMSEP"' or '"R2"'.

     _c_o_m_p_s the components specified, with '0' prepended if 'intercept'
          is 'TRUE'.

     _c_u_m_u_l_a_t_i_v_e 'TRUE' if 'comps' was 'NULL' or not specified.

     _c_a_l_l the function call

_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', 'crossval', 'mvrCv', 'validationplot', 'plot.mvrVal'

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

     data(oliveoil)
     mod <- plsr(sensory ~ chemical, ncomp = 4, data = oliveoil, validation = "LOO")
     RMSEP(mod)
     ## Not run: plot(R2(mod))

