Description Usage Arguments Details Note References See Also Examples
pairplot
creates a partial dependence plot to assess the effects of a
pair of predictor variables on the predictions of the ensemble. 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 
object 
an object of class 
varnames 
character vector of length two. Currently, pairplots can only
be requested for nonnominal variables. If varnames specifies the name(s) of
variables of class 
type 
character string. Type of plot to be generated.

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 2. For how many values of
x1 and x2 should partial dependence be plotted? If 
pred.type 
character string. Type of prediction to be plotted on zaxis.

... 
Additional arguments to be passed to 
By default, partial dependence will be plotted for each combination
of 20 values of the specified predictor variables. When nvals = NULL
is
specified a dependence plot will be created for every combination of the unique
observed values of the two predictor variables specified. Therefore, using
nvals = NULL
will often result in long computation times, and / or
memory allocation errors. Also, pre
ensembles derived
from training datasets that are very wide or long may result in long
computation times and / or memory allocation errors. In such cases, reducing
the values supplied to nvals
will reduce computation time and / or
memory allocation errors. When the nvals argument is supplied, values for the
minimum, maximum, and nvals  2 intermediate values of the predictor variable
will be plotted. Furthermore, if none of the variables specified appears in
the final prediction rule ensemble, an error will occur.
See also section 8.1 of Friedman & Popescu (2008).
Function pairplot
uses package akima to construct interpolated
surfaces and has an ACM license that restricts applications to noncommercial
usage, see
https://www.acm.org/publications/policies/softwarecopyrightnotice
Function pairplot
prints a note referring to this ACM licence.
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),])
pairplot(airq.ens, c("Temp", "Wind"))

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