forward.sel: Forward selection with multivariate Y using permutation under...

In adespatial: Multivariate Multiscale Spatial Analysis

Forward selection with multivariate Y using permutation under reducel model

Description

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

Usage

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,
  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
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
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
nperm	The number of permutation to be used.The default setting is 999 permutation.
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
alpha	Significance level. Stop the forward selection procedure if the p-value of a variable is higher than alpha. The default is 0.05 is TRUE
Xscale	Standardize the variables in table X to variance 1. The default setting is TRUE
Ycenter	Center the variables in table Y. The default setting is TRUE
Yscale	Standardize the variables in table Y to variance 1. 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.

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 [e-mail]

References

Canoco manual p.49

Examples

x <- matrix(rnorm(30),10,3)
y <- matrix(rnorm(50),10,5)
    
forward.sel(y,x,nperm=99, alpha = 0.5)
 

adespatial documentation built on Sept. 11, 2024, 7:04 p.m.