evimp {earth} | R Documentation |
Estimate variable importances in an earth
object
evimp(object, trim=TRUE, sqrt.=TRUE)
object |
An |
trim |
If |
sqrt. |
Default is |
This function returns a matrix showing the relative importances of the
variables in the model. There is a row for each variable. The row
name is the variable name, but with -unused
appended if the
variable does not appear in the final model.
The columns of the matrix are (not all of these are printed by print.evimp
):
col
: Column index of the variable in the x
argument to earth
.
used
: 1 if the variable is used in the final model, else 0.
Equivalently, 0 if the row name has an -unused
suffix.
nsubsets
: Variable importance using the "number of subsets" criterion.
Is the number of subsets that include the variable (see "Three Criteria" in the chapter
on evimp
in the earth
vignette
“Notes on the earth package”).
gcv
: Variable importance using the GCV criterion (see "Three Criteria").
gcv.match
: 1, except is
0 where the rank using the gcv
criterion differs from
that using the nsubsets
criterion.
In other words, there is a 0 for values that increase as you go
down the gcv
column.
rss
: Variable importance using the RSS criterion (see "Three Criteria").
rss.match
: Like gcv.match
but for the rss
.
The rows are sorted on the nsubsets
criterion.
This means that values in the nsubsets
column decrease as you go down the column
(more accurately, they are non-increasing).
The values in the gcv
and rss
columns
are also non-increasing, except where the
gcv
or rss
rank differs from the nsubsets
ranking.
There is a chapter on evimp
in the earth
package vignette
“Notes on the earth package”.
Acknowledgment
Thanks to Max Kuhn for the original evimp
code and for helpful discussions.
data(ozone1)
earth.mod <- earth(O3 ~ ., data=ozone1, degree=2)
ev <- evimp(earth.mod, trim=FALSE)
plot(ev)
print(ev)