# glpls1a.train.test.error

##### out-of-sample test set error using IRWPLS and IRWPLSF model

Out-of-sample test set error for fitting IRWPLS or IRWPLSF model on the training set for two-group classification

- Keywords
- regression

##### Usage

`glpls1a.train.test.error(train.X,train.y,test.X,test.y,K.prov=NULL,eps=1e-3,lmax=100,family="binomial",link="logit",br=T)`

##### Arguments

- train.X
- n by p design matrix (with no intercept term) for training set
- train.y
- response vector (0 or 1) for training set
- test.X
- transpose of the design matrix (with no intercept term) for test set
- test.y
- response vector (0 or 1) for test set
- K.prov
- number of PLS components, default is the rank of train.X
- eps
- tolerance for convergence
- lmax
- maximum number of iteration allowed
- 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

- error
- out-of-sample test error
- error.obs
- the misclassified error observation indices
- predict.test
- the predicted probabilities for test set

##### 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.cv.error`

,
`glpls1a.mlogit.cv.error`

, `glpls1a`

, `glpls1a.mlogit`

, `glpls1a.logit.all`

##### Examples

```
x <- matrix(rnorm(20),ncol=2)
y <- sample(0:1,10,TRUE)
x1 <- matrix(rnorm(10),ncol=2)
y1 <- sample(0:1,5,TRUE)
## no bias reduction
glpls1a.train.test.error(x,y,x1,y1,br=FALSE)
## bias reduction
glpls1a.train.test.error(x,y,x1,y1,br=TRUE)
```

*Documentation reproduced from package gpls, version 1.44.0, License: Artistic-2.0*

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