# forward.sel: Forward selection with multivariate Y using a parametric... In packfor: Forward Selection with permutation (Canoco p.46)

## Description

Performs a forward selection by permutation of residuals under reduced model. Y can be multivariate.

## Usage

 ```1 2 3 4``` ```forward.sel(Y, X, K = nrow(X) - 1, R2thresh = 0.99, adjR2thresh = 0.99,nperm = 999, R2more = 0.001, alpha = 0.05, Xscale = TRUE, Ycenter = TRUE, Yscale = FALSE) forward.sel.par(Y, X, alpha = 0.05, K = nrow(X)-1, R2thresh = 0.99, R2more = 0.001, adjR2thresh = 0.99, Yscale = FALSE, verbose=TRUE) ```

## Arguments

 `Y` A matrix of n lines and m columns that contains (numeric) response variables. `X` A matrix of n lines and p columns that contains (numeric) explanatory variables. `K` This number is the number of variables to be selected in the forward selection. The default setting is one minus the number of row.(See details for more information) `R2thresh` The number given here is a R2 parameter. If the forward selection has a selection of variable which represent the number presented in the parameter or higher, after the introduction of a variable, the forward selection will stop. The setting of this parameter varies from 0.01 to 1. (See details for more information) `adjR2thresh` The number given here is a adjusted R2 parameter. If the forward selection has a selection of variable which represent the number presented in the parameter or higher, after the introduction of a variable, the forward selection will stop. The setting of this parameter varies from 0.01 to 1. (See details for more information) `nperm` The number of permutation to be done on the forward selection. the default setting is 999 permutation. `R2more` The number given here is a R2 parameter. If the forward selection gets to a point where the R2 given by a variable is lower than R2more it will stops. The default setting is 0.001. (See details for more information) `alpha` The number given here is a significance level. If the p-value of a variable is higher than alpha, the procedure stops. The default setting is 0.05. (See details for more information) `Xscale` This parameter scales the data entered as parameter X. The default setting is TRUE `Ycenter` This parameter centers the data entered as parameter Y. The default setting is TRUE `Yscale` This parameter scales the data entered as parameter Y. The default setting is FALSE `verbose` If 'TRUE' more diagnostics are printed. The default setting is TRUE

## Details

The forward selection will stop when either K, R2tresh, adjR2tresh, alpha and R2more has its parameter reached. The parametric test for the increase in R-square statistic in forward selection, as implemented in the function `forward.sel.par`, can be applied as follows.

(a) If Y is univariate, this function implements the standard parametric F-test used in forward selection (FS) in multiple regression.

(b) If Y is multivariate, this function implements FS using the modified F-test described by Miller and Farr (1971). This test requires that

– the Y variables be standardized,

– the error in the response variables be normally distributed. This condition must be verified by the user.

## Value

A dataframe with:

 ` variables ` The names of the variables ` order ` The order of the selection of the variables ` R2 ` The R2 of the variable selected ` R2Cum ` The cumulative R2 of the variables selected ` AdjR2Cum ` The cumulative adjusted R2 of the variables selected ` F ` The F statistic ` pval ` The P-value statistic

## Note

Not yet implemented for CCA (weighted regression) and with covariables.

## Author(s)

Stephane Dray. For the parametric method, original code of Pierre Legendre and Guillaume Blanchet.

## References

Canoco manual p.49
Miller, J. K., and S. D. Farr. (1971). Bimultivariate redundancy: a comprehensive measure of interbattery relationship. Multivariate Behavioral Research, 6, 313–324.

## Examples

 ```1 2 3 4 5``` ``` x=matrix(rnorm(30),10,3) y=matrix(rnorm(50),10,5) forward.sel(y,x,nperm=99, alpha = 0.5) ```

packfor documentation built on May 31, 2017, 4:32 a.m.