glpls1a {gpls} | R Documentation |

## Fit IRWPLS and IRWPLSF model

### Description

Fit Iteratively ReWeighted Least Squares (IRWPLS) with an option of
Firth's bias reduction procedure (IRWPLSF) for two-group classification

### Usage

glpls1a(X, y, K.prov = NULL, eps = 0.001, lmax = 100, b.ini = NULL,
denom.eps = 1e-20, family = "binomial", link = NULL, br = TRUE)

### Arguments

`X` |
n by p design matrix (with no intercept term) |

`y` |
response vector 0 or 1 |

`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 |

### Details

### Value

`coefficients ` |
regression coefficients |

`convergence ` |
whether convergence is achieved |

`niter` |
total number of iterations |

`bias.reduction` |
whether Firth's procedure is used |

### Note

### Author(s)

Beiying Ding, Robert 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.mlogit`

, `glpls1a.logit.all`

,
`glpls1a.train.test.error`

,
`glpls1a.cv.error`

, `glpls1a.mlogit.cv.error`

### Examples

x <- matrix(rnorm(20),ncol=2)
y <- sample(0:1,10,TRUE)
## no bias reduction
glpls1a(x,y,br=FALSE)
## no bias reduction and 1 PLS component
glpls1a(x,y,K.prov=1,br=FALSE)
## bias reduction
glpls1a(x,y,br=TRUE)

[Package

*gpls* version 1.0.6

Index]