easyalluvial (version 0.2.2)

alluvial_model_response_caret: create model response plot for caret models

Description

Wraps alluvial_model_response and get_data_space into one call for caret models.

Usage

alluvial_model_response_caret(
  train,
  degree = 4,
  bins = 5,
  bin_labels = c("LL", "ML", "M", "MH", "HH"),
  col_vector_flow = c("#FF0065", "#009850", "#A56F2B", "#005EAA", "#710500", "#7B5380",
    "#9DD1D1"),
  method = "median",
  params_bin_numeric_pred = list(center = T, transform = T, scale = T),
  pred_train = NULL,
  stratum_label_size = 3.5,
  force = F,
  ...
)

Arguments

train

caret train object

degree

integer, number of top important variables to select. For plotting more than 4 will result in two many flows and the alluvial plot will not be very readable, Default: 4

bins

integer, number of bins for numeric variables, increasing this number might result in too many flows, Default: 5

bin_labels

labels for the bins from low to high, Default: c("LL", "ML", "M", "MH", "HH")

col_vector_flow,

character vector, defines flow colours, Default: c('#FF0065','#009850', '#A56F2B', '#005EAA', '#710500')

method,

character vector, one of c('median', 'pdp')

median

sets variables that are not displayed to median mode, use with regular predictions

pdp

partial dependency plot method, for each observation in the training data the displayed variableas are set to the indicated values. The predict function is called for each modified observation and the result is averaged

. Default: 'median'

params_bin_numeric_pred

list, additional parameters passed to manip_bin_numerics which is applied to the pred parameter. Default: list( bins = 5, center = T, transform = T, scale = T)

pred_train

numeric vector, base the automated binning of the pred vector on the distribution of the training predictions. This is useful if marginal histograms are added to the plot later. Default = NULL

stratum_label_size

numeric, Default: 3.5

force

logical, force plotting of over 1500 flows, Default: FALSE

...

additional parameters passed to alluvial_wide

Value

ggplot2 object

Details

this model visualisation approach follows the "visualising the model in the dataspace" principle as described in Wickham H, Cook D, Hofmann H (2015) Visualizing statistical models: Removing the blindfold. Statistical Analysis and Data Mining 8(4) <doi:10.1002/sam.11271>

See Also

alluvial_wide, get_data_space, varImp, extractPrediction, get_data_space, get_pdp_predictions

Examples

# NOT RUN {
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]

train = caret::train( disp ~ .
                     , df
                     , method = 'rf'
                     , trControl = caret::trainControl( method = 'none' )
                     , importance = TRUE )

alluvial_model_response_caret(train, degree = 3)

# partial dependency plotting method
# }
# NOT RUN {
alluvial_model_response_caret(train, degree = 3, method = 'pdp')
 
# }