pairplot: Create partial dependence plot for a pair of predictor variables in a prediction rule ensemble (pre)

Description

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.

Usage

pairplot(object, varnames, type = "both",
  penalty.par.val = "lambda.1se", nvals = c(20L, 20L),
  pred.type = "response", ...)

Arguments

object

an object of class pre

varnames

character vector of length two. Currently, pairplots can only be requested for non-nominal variables. If varnames specifies the name(s) of variables of class "factor", an error will be printed.

type

character string. Type of plot to be generated. type = "heatmap" yields a heatmap plot, type = "contour" yields a contour plot, type = "both" yields a heatmap plot with added contours, type = "perspective" yields a three dimensional plot.

penalty.par.val

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).

nvals

optional numeric vector of length 2. For how many values of x1 and x2 should partial dependence be plotted? If NULL, all observed values for the two predictor variables specified will be used (see details).

pred.type

character string. Type of prediction to be plotted on z-axis. pred.type = "response" gives fitted values for continuous outputs and fitted probabilities for nominal outputs. pred.type = "link" gives fitted values for continuous outputs and linear predictor values for nominal outputs.

...

Additional arguments to be passed to image, contour or persp (depending on whether type is specified to be "heatmap", "contour", "both" or "perspective").

Details

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).

References

Fokkema, M. (2018). Fitting prediction rule ensembles with R package pre. https://arxiv.org/abs/1707.07149.

Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916-954.

Milborrow, S. (2019). plotmo: Plot a model's residuals, response, and partial dependence plots. https://CRAN.R-project.org/package=plotmo

Examples

Run this code

# NOT RUN {
set.seed(42)
airq.ens <- pre(Ozone ~ ., data = airquality[complete.cases(airquality),])
pairplot(airq.ens, c("Temp", "Wind"))
# }