# simpls.fit: Sijmen de Jong's SIMPLS In bhmevik/pls: Partial Least Squares and Principal Component Regression

 simpls.fit R Documentation

## Sijmen de Jong's SIMPLS

### Description

Fits a PLSR model with the SIMPLS algorithm.

### Usage

```simpls.fit(X, Y, ncomp, center = TRUE, stripped = FALSE, ...)
```

### Arguments

 `X` a matrix of observations. `NA`s and `Inf`s are not allowed. `Y` a vector or matrix of responses. `NA`s and `Inf`s are not allowed. `ncomp` the number of components to be used in the modelling. `center` logical, determines if the X and Y matrices are mean centered or not. Default is to perform mean centering. `stripped` logical. If `TRUE` the calculations are stripped as much as possible for speed; this is meant for use with cross-validation or simulations when only the coefficients are needed. Defaults to `FALSE`. `...` other arguments. Currently ignored.

### Details

This function should not be called directly, but through the generic functions `plsr` or `mvr` with the argument `method="simpls"`. SIMPLS is much faster than the NIPALS algorithm, especially when the number of X variables increases, but gives slightly different results in the case of multivariate Y. SIMPLS truly maximises the covariance criterion. According to de Jong, the standard PLS2 algorithms lie closer to ordinary least-squares regression where a precise fit is sought; SIMPLS lies closer to PCR with stable predictions.

### Value

A list containing the following components is returned:

 `coefficients` an array of regression coefficients for 1, ..., `ncomp` components. The dimensions of `coefficients` are `c(nvar, npred, ncomp)` with `nvar` the number of `X` variables and `npred` the number of variables to be predicted in `Y`. `scores` a matrix of scores. `loadings` a matrix of loadings. `Yscores` a matrix of Y-scores. `Yloadings` a matrix of Y-loadings. `projection` the projection matrix used to convert X to scores. `Xmeans` a vector of means of the X variables. `Ymeans` a vector of means of the Y variables. `fitted.values` an array of fitted values. The dimensions of `fitted.values` are `c(nobj, npred, ncomp)` with `nobj` the number samples and `npred` the number of Y variables. `residuals` an array of regression residuals. It has the same dimensions as `fitted.values`. `Xvar` a vector with the amount of X-variance explained by each component. `Xtotvar` Total variance in `X`.

If `stripped` is `TRUE`, only the components `coefficients`, `Xmeans` and `Ymeans` are returned.

### Author(s)

Ron Wehrens and Bjørn-Helge Mevik

### References

de Jong, S. (1993) SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18, 251–263.

`mvr` `plsr` `pcr` `kernelpls.fit` `widekernelpls.fit` `oscorespls.fit`