pls (version 2.1-0)

mvr: Partial Least Squares and Principal Component Regression

Description

Functions to perform partial least squares regression (PLSR) or principal component regression (PCR), with a formula interface. Cross-validation can be used. Prediction, model extraction, plot, print and summary methods exist.

Usage

mvr(formula, ncomp, data, subset, na.action,
    method = pls.options()$mvralg,
    scale = FALSE, validation = c("none", "CV", "LOO"),
    model = TRUE, x = FALSE, y = FALSE, ...)
plsr(..., method = pls.options()$plsralg)
pcr(..., method = pls.options()$pcralg)

Value

• If method = "model.frame", the model frame is returned. Otherwise, an object of class mvr is returned. The object contains all components returned by the underlying fit function. In addition, it contains the following components:
• validation
  if validation was requested, the results of the cross-validation. See mvrCv for details.
• na.action
  if observations with missing values were removed, na.action contains a vector with their indices. The class of this vector is used by functions like fitted to decide how to treat the observations.
• ncomp
  the number of components of the model.
• method
  the method used to fit the model. See the argument method for possible values.
• scale
  if scaling was requested (with scale), the scaling used.
• call
  the function call.
• terms
  the model terms.
• model
  if model = TRUE, the model frame.
• x
  if x = TRUE, the model matrix.
• y
  if y = TRUE, the model response.

encoding

latin1

Details

The functions fit PLSR or PCR models with 1, $\ldots$, ncomp number of components. Multi-response models are fully supported.

The type of model to fit is specified with the method argument. Four PLSR algorithms are available: the kernel algorithm ("kernelpls"), the wide kernel algorithm ("widekernelpls"), SIMPLS ("simpls") and the classical orthogonal scores algorithm ("oscorespls"). One PCR algorithm is available: using the singular value decomposition ("svdpc"). If method is "model.frame", the model frame is returned. The functions pcr and plsr are wrappers for mvr, with different values for method.

The formula argument should be a symbolic formula of the form response ~ terms, where response is the name of the response vector or matrix (for multi-response models) and terms is the name of one or more predictor matrices, usually separated by +, e.g., water ~ FTIR or y ~ X + Z. See lm for a detailed description. The named variables should exist in the supplied data data frame or in the global environment. Note: Do not use mvr(mydata$y ~ mydata$X, ...), instead use mvr(y ~ X, data = mydata, ...). Otherwise, predict.mvr will not work properly. The chapter Statistical models in R of the manual An Introduction to R distributed with Ris a good reference on formulas in R.

The number of components to fit is specified with the argument ncomp. It this is not supplied, the maximal number of components is used (taking account of any cross-validation).

If validation = "CV", cross-validation is performed. The number and type of cross-validation segments are specified with the arguments segments and segment.type. See mvrCv for details. If validation = "LOO", leave-one-out cross-validation is performed. It is an error to specify the segments when validation = "LOO" is specified.

Note that the cross-validation is optimised for speed, and some generality has been sacrificed. Especially, the model matrix is calculated only once for the complete cross-validation, so models like y ~ msc(X) will not be properly cross-validated. However, scaling requested by scale = TRUE is properly cross-validated. For proper cross-validation of models where the model matrix must be updated/regenerated for each segment, use the separate function crossval.

References

Martens, H., N�s, T. (1989) Multivariate calibration. Chichester: Wiley.

See Also

kernelpls.fit, widekernelpls.fit, simpls.fit, oscorespls.fit, svdpc.fit, mvrCv, crossval, loadings, scores, loading.weights, coef.mvr, predict.mvr, R2, MSEP, RMSEP, plot.mvr

Examples

data(yarn)
## Default methods:
yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV")
yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV")

## Alternative methods:
yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV",
                      method = "oscorespls")
yarn.simpls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV",
                  method = "simpls")

data(oliveoil)
sens.pcr <- pcr(sensory ~ chemical, ncomp = 4, scale = TRUE, data = oliveoil)
sens.pls <- plsr(sensory ~ chemical, ncomp = 4, scale = TRUE, data = oliveoil)