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).
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
)
A dataframe with:
The names of the variables
The order of the selection of the variables
The R2 of the variable selected
The cumulative R2 of the variables selected
The cumulative adjusted R2 of the variables selected
The F statistic
The P-value statistic
Response data matrix with n rows and m columns containing quantitative variables
Explanatory data matrix with n rows and p columns containing quantitative variables
Significance level. Stop the forward selection procedure if the p-value of a variable is higher than alpha. The default is 0.05
Maximum number of variables to be selected. The default is one minus the number of rows
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
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
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
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)
If 'TRUE' more diagnostics are printed. The default setting is TRUE
Pierre Legendre pierre.legendre@umontreal.ca and Guillaume Blanchet
The forward selection will stop when either K, R2tresh, adjR2tresh, alpha and R2more has its parameter reached.
Miller, J. K. & S. D. Farr. 1971. Bimultivariate redundancy: a
comprehensive measure of interbattery relationship. Multivariate
Behavioral Research, 6, 313--324.
x <- matrix(rnorm(30),10,3)
y <- matrix(rnorm(50),10,5)
forward.sel.par(y,x, alpha = 0.5)
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