create model response plot

alluvial plots are capable of displaying higher dimensional data on a plane, thus lend themselves to plot the response of a statistical model to changes in the input data across multiple dimensions. The practical limit here is 4 dimensions. We need the data space (a sensible range of data calculated based on the importance of the explanatory variables of the model as created by get_data_space and the predictions returned by the model in response to the data space.

alluvial_model_response(
  pred,
  dspace,
  imp,
  degree = 4,
  bin_labels = c("LL", "ML", "M", "MH", "HH"),
  col_vector_flow = c("#FF0065", "#009850", "#A56F2B", "#005EAA", "#710500", "#7B5380",
    "#9DD1D1"),
  method = "median",
  force = FALSE,
  params_bin_numeric_pred = list(bins = 5),
  pred_train = NULL,
  stratum_label_size = 3.5,
  ...
)

Arguments

pred

vector, predictions, if method = 'pdp' use get_pdp_predictions to calculate predictions

dspace

data frame, returned by get_data_space

imp

dataframe, with not more then two columns one of them numeric containing importance measures and one character or factor column containing corresponding variable names as found in training data.

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

bin_labels

labels for prediction 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 variable as are set to the indicated values. The predict function is called for each modified observation and the result is averaged, calculate predictions using get_pdp_predictions

. Default: 'median'

force

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

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

...

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, alluvial_model_response_caret

Examples

df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp, degree = 3)
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `cyl = (function (f, na_level = "(Missing)") ...`.
#> Caused by warning:
#> ! `fct_explicit_na()` was deprecated in forcats 1.0.0.
#> ℹ Please use `fct_na_value_to_level()` instead.
#> ℹ The deprecated feature was likely used in the easyalluvial package.
#>   Please report the issue to the authors.
pred = predict(m, newdata = dspace)
alluvial_model_response(pred, dspace, imp, degree = 3)


# partial dependency plotting method
if (FALSE) {
 pred = get_pdp_predictions(df, imp
                            , .f_predict = randomForest:::predict.randomForest
                            , m
                            , degree = 3
                            , bins = 5)


 alluvial_model_response(pred, dspace, imp, degree = 3, method = 'pdp')
 }