Calculates the Accumulated Local Effects (ALE) from an ERF object
calc_ALE(
fit,
var,
save = TRUE,
out.folder = NULL,
cores = parallel::detectCores() - 4,
type = "response",
K = 50
)The fitted object returned from calling ens_random_forests()
The name of the response variable
A logical flag to save the output as an RData object, default is TRUE.
A path to the folder to write out too. If NULL then a folder is generated in the working directory
An integer value that either indicates the number of cores to use for parallel processing or a negative value to indicate the number of cores to leave free. Default is to leave two cores free.
is either 'response' or 'prob' from predict.randomForest; if 'prob' then n sets of predictions are returned for the n levels in var; if "response" then the factorized predicted response values are returned
A list that contains a data.frame for each variable, ordered by the mean variable importance, and a vector of the covariate values (used for rug plot in plot_ALE). The columns in each data.frame are as follows:
x: the covariate values that the ALE was calculated for
class: the class of the covariate; used by subsequent plot_ALE function
q: the quantile of the x value of the covariate
f.X: the ALEs evaluated at a given x value
#run an ERF with 10 RFs and
ens_rf_ex <- ens_random_forests(df=simData$samples, var="obs", covariates=grep("cov", colnames(simData$samples),value=T), save=FALSE, cores=1)
#> rounding n.forests to the nearest one
ALEdf <- calc_ALE(ens_rf_ex, save=FALSE)
#> No name of response variable, making one
#> rounding n.forests to the nearest one
head(ALEdf[[1]]$df)
#> x class q f.1 f.2 f.3 f.4
#> 1 -0.6322850 numeric 0.0002 -0.3150314 -0.3059006 -0.2780686 -0.3339576
#> 2 -0.4659179 numeric 0.0202 -0.3150314 -0.3059006 -0.2780686 -0.3339576
#> 3 -0.4094575 numeric 0.0401 -0.3150314 -0.3059006 -0.2780686 -0.3339576
#> 4 -0.3715804 numeric 0.0600 -0.3150314 -0.3059006 -0.2780686 -0.3339576
#> 5 -0.3321141 numeric 0.0800 -0.3150314 -0.3059006 -0.2780686 -0.3339576
#> 6 -0.2917534 numeric 0.1001 -0.3150314 -0.3059006 -0.2780686 -0.3339576
#> f.5 f.6 f.7 f.8 f.9 f.10
#> 1 -0.3081547 -0.2988771 -0.3124441 -0.2597704 -0.3602964 -0.3139273
#> 2 -0.3081547 -0.2988771 -0.3124441 -0.2597704 -0.3602964 -0.3139273
#> 3 -0.3081547 -0.2988771 -0.3124441 -0.2597704 -0.3602964 -0.3139273
#> 4 -0.3081547 -0.2988771 -0.3124441 -0.2597704 -0.3602964 -0.3139273
#> 5 -0.3081547 -0.2988771 -0.3124441 -0.2597704 -0.3602964 -0.3139273
#> 6 -0.3081547 -0.2988771 -0.3124441 -0.2597704 -0.3602964 -0.3139273