adespatial (version 0.0-7)

forward.sel.par: Parametric forward selection of explanatory variables in regression and RDA

Description

If Y is univariate, this function implements FS in regression. If Y is multivariate, this function implements FS using the F-test described by Miller and Farr (1971). This test requires that (i) the Y variables be standardized, and (ii) the error in the response variables be normally distributed (to be verified by the user).

Usage

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

Response data matrix with n rows and m columns containing quantitative variables

X

Explanatory data matrix with n rows and p columns containing quantitative variables

alpha

Significance level. Stop the forward selection procedure if the p-value of a variable is higher than alpha. The default is 0.05

K

Maximum number of variables to be selected. The default is one minus the number of rows

R2thresh

Stop the forward selection procedure if the R-square of the model exceeds the stated value. This parameter can vary from 0.001 to 1

R2more

Stop the forward selection procedure if the difference in model R-square with the previous step is lower than R2more. The default setting is 0.001

adjR2thresh

Stop the forward selection procedure if the adjusted R-square of the model exceeds the stated value. This parameter can take any value (positive or negative) smaller than 1

Yscale

Standardize the variables in table Y to variance 1. The default setting is FALSE. The setting is automatically changed to TRUE if Y contains more than one variable. This is a validity condition for the parametric test of significance (Miller and Farr 1971)

verbose

If 'TRUE' more diagnostics are printed. The default setting is TRUE

Value

A dataframe with: A dataframe with:

Details

The forward selection will stop when either K, R2tresh, adjR2tresh, alpha and R2more has its parameter reached.

References

Miller, J. K. & S. D. Farr. 1971. Bimultivariate redundancy: a comprehensive measure of interbattery relationship. Multivariate Behavioral Research, 6, 313--324.

Examples

x <- matrix(rnorm(30),10,3)
y <- matrix(rnorm(50),10,5)
    
forward.sel.par(y,x, alpha = 0.5)