Description Usage Arguments Details References See Also Examples
singleplot
creates a partial dependence plot, which shows the effect of
a predictor variable on the ensemble's predictions. Note that plotting partial
dependence is computationally intensive. Computation time will increase fast
with increasing numbers of observations and variables. For large
datasets, package 'plotmo' (Milborrow, 2019) provides more efficient functions
for plotting partial dependence and also supports 'pre' models.
1 2 3 4 5 6 7 8 9  singleplot(
object,
varname,
penalty.par.val = "lambda.1se",
nvals = NULL,
type = "response",
ylab = "predicted",
...
)

object 
an object of class 
varname 
character vector of length one, specifying the variable for
which the partial dependence plot should be created. Note that 
penalty.par.val 
character or numeric. Value of the penalty parameter
λ to be employed for selecting the final ensemble. The default

nvals 
optional numeric vector of length one. For how many values of x should the partial dependence plot be created? 
type 
character string. Type of prediction to be plotted on yaxis.

ylab 
character. Label to be printed on the yaxis. 
... 
Further arguments to be passed to

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 printed.
See also section 8.1 of Friedman & Popescu (2008).
Fokkema, M. (2020). Fitting prediction rule ensembles with R package pre. Journal of Statistical Software, 92(12), 130. https://doi.org/10.18637/jss.v092.i12
Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916954.
Milborrow, S. (2019). plotmo: Plot a model's residuals, response, and partial dependence plots. https://CRAN.Rproject.org/package=plotmo
1 2 3  set.seed(42)
airq.ens < pre(Ozone ~ ., data = airquality[complete.cases(airquality),])
singleplot(airq.ens, "Temp")

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