singleplot creates a partial dependence plot, which shows the effect of
a predictor variable on the ensemble's predictions
singleplot(object, varname, penalty.par.val = "lambda.1se", nvals = NULL,
type = "response", ...)an object of class pre
character vector of length one, specifying the variable for
which the partial dependence plot should be created.
penalty.par.val character. Penalty parameter criterion to be used for
selecting final model: lambda giving minimum cv error ("lambda.min") or lambda
giving cv error that is within 1 standard error of minimum cv error
("lambda.1se"). Alternatively, a numeric value may be specified,
corresponding to one of the values of lambda in the sequence used by glmnet,
for which estimated cv error can be inspected by running
object$glmnet.fit and plot(object$glmnet.fit).
character. Penalty parameter criterion to be used for
selecting final model: lambda giving minimum cv error ("lambda.min") or
lambda giving cv error that is within 1 standard error of minimum cv error
("lambda.1se"). Alternatively, a numeric value may be specified,
corresponding to one of the values of lambda in the sequence used by glmnet,
for which estimated cv error can be inspected by running
object$glmnet.fit and plot(object$glmnet.fit).
optional numeric vector of length one. For how many values of x should the partial dependence plot be created?
character string. Type of prediction to be plotted on y-axis.
type = "response" gives fitted values for continuous outputs and
fitted probabilities for nominal outputs. type = "link" gives fitted
values for continuous outputs and linear predictor values for nominal outputs.
Further arguments to be passed to
plot.default.
By default, a partial dependence plot will be created for each unique
observed value of the specified predictor variable. When the number of unique
observed values is large, this may take a long time to compute. In that case,
specifying the nvals argument can substantially reduce computing time. When the
nvals argument is supplied, values for the minimum, maximum, and (nvals - 2)
intermediate values of the predictor variable will be plotted. Note that nvals
can be specified only for numeric and ordered input variables. If the plot is
requested for a nominal input variable, the nvals argument will be
ignored and a warning is printed.
# NOT RUN {
set.seed(42)
airq.ens <- pre(Ozone ~ ., data = airquality[complete.cases(airquality),])
singleplot(airq.ens, "Temp")
# }
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