forward.sel {adespatial} | R Documentation |
Performs a forward selection by permutation of residuals under reduced model. Y can be multivariate.
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 )
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 |
The forward selection will stop when either K, R2tresh, adjR2tresh, alpha and R2more has its parameter reached.
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 |
Not yet implemented for CCA (weighted regression) and with covariables.
Stephane Dray stephane.dray@univ-lyon1.fr
Canoco manual p.49
x <- matrix(rnorm(30),10,3) y <- matrix(rnorm(50),10,5) forward.sel(y,x,nperm=99, alpha = 0.5)