glpls1a.logit.all {gpls} | R Documentation |

## Fit MIRWPLS and MIRWPLSF model separately for logits

### Description

Apply Iteratively ReWeighted Least Squares (MIRWPLS) with an
option of Firth's bias reduction procedure (MIRWPLSF) for multi-group
(say C+1 classes) classification by fitting logit models for all C
classes vs baseline class separately.

### Usage

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

### Arguments

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

`y` |
response vector with class lables 1 to C+1 for C+1 group
classification, baseline class should be 1 |

`K.prov` |
number of PLS components |

`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` (i.e. multinomial here) 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 coefficient matrix |

### 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`

,`glpls1a.mlogit.cv.error`

,
`glpls1a.train.test.error`

,
`glpls1a.cv.error`

### Examples

x <- matrix(rnorm(20),ncol=2)
y <- sample(1:3,10,TRUE)
## no bias reduction
glpls1a.logit.all(x,y,br=FALSE)
## bias reduction
glpls1a.logit.all(x,y,br=TRUE)

[Package

*gpls* version 1.0.6

Index]