Description Usage Arguments Value See Also Examples
This function computes for a given regression method
("linreg"
, "pcr"
, "plsr"
, "ridge"
,"sir"
,
"rf"
, ) the importance
of the covariates by estimating the response variable with some perturbations
of the covariates and computing the error due to these perturbations.
The variable importance (VI) of a covariate is the mean square error (MSE) when
the values of this variable are randomly permuted. Covariates with higher VI are then more
important to predict the response variable. This procedure is replicated several times
giving several values of VI for each covariate. The original MSE (with no perturbation of the data)
is also performed for comparison purpose.
1 | varimportance(X, Y, method = "linreg", nperm = 10)
|
X |
a numerical matrix containing the |
Y |
a numerical response vector. |
method |
a regression method
( |
nperm |
the number of random perturbations to perform the importance of the covariates (VI). |
An object with S3 class "varimportance" and the following components:
mat_imp |
a matrix of dimension |
base_imp |
the original mean square error. |
plot.varimportance
, select.varimportance
, choicemod
1 2 3 4 5 6 7 |
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