easyalluvial: alluvial_model_response – R documentation

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alluvial_model_response

create model response plot

Description

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.

Usage

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`

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>

Value

ggplot2 object

Examples

df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp, degree = 3)
pred = predict(m, newdata = dspace)
alluvial_model_response(pred, dspace, imp, degree = 3)

# partial dependency plotting method
## Not run: 
 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')
 
## End(Not run)

easyalluvial

Generate Alluvial Plots with a Single Line of Code

v0.3.0

CC0

Authors

Bjoern Koneswarakantha [aut, cre] (<https://orcid.org/0000-0003-4585-7799>)

Initial release