coef.pre
returns coefficients for prediction rules and linear terms in
the final ensemble
# S3 method for pre
coef(object, penalty.par.val = "lambda.1se", ...)
object of class pre
character or numeric. Value of the penalty parameter
\(\lambda\) to be employed for selecting the final ensemble. The default
"lambda.min"
employs the \(\lambda\) value within 1 standard
error of the minimum cross-validated error. Alternatively,
"lambda.min"
may be specified, to employ the \(\lambda\) value
with minimum cross-validated error, or a numeric value \(>0\) may be
specified, with higher values yielding a sparser ensemble. To evaluate the
trade-off between accuracy and sparsity of the final ensemble, inspect
pre_object$glmnet.fit
and plot(pre_object$glmnet.fit)
.
additional arguments to be passed to coef.cv.glmnet
.
returns a dataframe with 3 columns: coefficient, rule (rule or
variable name) and description (NA
for linear terms, conditions for
rules).
In some cases, duplicated variable names may appear in the model. For example, the first variable is a factor named 'V1' and there are also variables named 'V10' and/or 'V11' and/or 'V12' (etc). Then for for the binary factor V1, dummy contrast variables will be created, named 'V10', 'V11', 'V12' (etc). As should be clear from this example, this yields duplicated variable names, which may yield problems, for example in the calculation of predictions and importances, later on. This can be prevented by renaming factor variables with numbers in their name, prior to analysis.
pre
, plot.pre
,
cvpre
, importance
, predict.pre
,
interact
, print.pre
# NOT RUN {
set.seed(42)
airq.ens <- pre(Ozone ~ ., data = airquality[complete.cases(airquality),])
coefs <- coef(airq.ens)
# }
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