get_data_space: calculate data space

Description Usage Arguments Details Value See Also Examples


calculates a dataspace based on the modelling dataframe and the importance of the explanatory variables. It only considers the most important variables as defined by the degree parameter. It selects a number (defined by bins) of sensible single values spread over the range of the numeric variables and creates all possible value combinations among the most important variables. The values of the remaining variables are set to mode(factors) or median(numerics).


get_data_space(df, imp, degree = 4, bins = 5, max_levels = 10)



dataframe, training data


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.


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


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


integer, maximum number of levels per factor variable, Default: 10


It selects a the top most important variables based on the degree parameter and bins the numeric variables using manip_bin_numerics, while leaving categoric variables unchanged. The number of bins for each numeric variable is set to bins -2. Next the median is picked for each of the bins and the min and the max value is added for each numeric variable So that we get median(bin) X bins -2, max, min for each numeric variable. Then all possible combinations between those values and the categoric factor levels are created. The total number of all possible combinations defines the range of the data space. The values of the remaining variables are set to mode(factors) or median(numerics).

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>


data frame

See Also

alluvial_wide, manip_bin_numerics


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

erblast/easyalluvial documentation built on Aug. 8, 2020, 1:11 a.m.