gpls {gpls} | R Documentation |

## A function to fit Generalized partial least squares models.

### Description

Partial least squares is a commonly used dimension reduction
technique. The paradigm can be extended to include generalized linear
models in several different ways. The code in this function uses the
extension proposed by Ding and Gentleman, 2004.

### Usage

gpls(x, ...)
## Default S3 method:
gpls(x, y, K.prov=NULL, eps=1e-3, lmax=100, b.ini=NULL,
denom.eps=1e-20, family="binomial", link=NULL, br=TRUE, ...)
## S3 method for class 'formula':
gpls(formula, data, contrasts=NULL, K.prov=NULL,
eps=1e-3, lmax=100, b.ini=NULL, denom.eps=1e-20, family="binomial",
link=NULL, br=TRUE, ...)

### Arguments

`x` |
The matrix of covariates. |

`formula` |
A formula of the form 'y ~ x1 + x2 + ...', where
`y` is the response and the other terms are covariates. |

`y` |
The vector of responses |

`data` |
A data.frame to resolve the forumla, if used |

`K.prov` |
number of PLS components, default is the rank of X |

`eps` |
tolerance for convergence |

`lmax` |
maximum number of iteration allowed |

`b.ini` |
initial value of regression coefficients |

`denom.eps` |
small quanitity to guarantee nonzero denominator in
deciding convergence |

`family` |
glm family, `binomial` is the only relevant one here |

`link` |
link function, `logit` is the only one practically
implemented now |

`br` |
TRUE if Firth's bias reduction procedure is used |

`...` |
Additional arguements. |

`contrasts` |
an optional list. See the `contrasts.arg` of
`model.matrix.default` . |

### Details

This is a different interface to the functionality provided by
`glpls1a`

. The interface is intended to be simpler to use
and more consistent with other matchine learning code in R.

The technology is intended to deal with two class problems where
there are more predictors than cases. If a response variable
(`y`

) is used that has more than two levels the behavior may
be unusual.

### Value

An object of class `gpls`

with the following components:

`coefficients` |
The estimated coefficients. |

`convergence` |
A boolean indicating whether convergence was
achieved. |

`niter` |
The total number of iterations. |

`bias.reduction` |
A boolean indicating whether Firth's procedure
was used. |

`family` |
The `family` argument that was passed in. |

`link` |
The `link` argument that was passed in. |

`call` |
The call |

`levs` |
The factor levels for prediction. |

### Author(s)

B. Ding and R. Gentleman

### References

Ding, B.Y. and Gentleman, R. (2003)Classification
using generalized partial least squares.
Marx, B.D (1996)Iteratively reweighted partial least squares
estimation for generalized linear regression. Technometrics 38(4):
374-381.
### See Also

`glpls1a`

### Examples

library(MASS)
m1 = gpls(type~., data=Pima.tr, K=3)

[Package

*gpls* version 1.0.6

Index]