getVariableImportance: Variable importance for Machine Learning models

In SEMdeep: Structural Equation Modeling with Deep Neural Network and Machine Learning

Variable importance for Machine Learning models

Description

Extraction of ML variable importance measures.

Usage

getVariableImportance(object, thr = NULL, verbose = FALSE, ...)

Arguments

object	A model fitting object from SEMml() function.
thr	A numeric value [0-1] indicating the threshold to apply to the variable importance values to color the graph. If thr = NULL (default), the threshold is set to thr = 0.5*max(abs(variable importance values)).
verbose	A logical value. If FALSE (default), the processed graph will not be plotted to screen.
...	Currently ignored.

Details

The variable (predictor) importance will be computed considering: (i) the absolute value of the z-statistic of the model parameters for "sem"; (ii) the variable importance measures from the rpart, importance or xgb.importance functions for "tree", "rf" or "xgb"; and (iii) the Olden's connection weights for "nn" or "dnn" methods.

Value

A list of three object: (i) est: a data.frame including the connections together with their variable importances (VarImp)), (ii) gest: if the outcome vector is given, a data.frame of VarImp for outcome lavels, and (iii) dag: DAG with colored edges/nodes. If abs(VarImp) > thr will be highlighted in red (VarImp > 0) or blue (VarImp < 0). If the outcome vector is given, nodes with variable importances summed over the outcome levels, i.e. sum(VarImp[outcome levels])) > thr, will be highlighted in pink.

Author(s)

Mario Grassi [e-mail]

References

add references

Examples

# load ALS data
ig<- alsData$graph
data<- alsData$exprs
data<- transformData(data)$data

#ncores<- parallel::detectCores(logical = FALSE)
ml0<- SEMml(ig, data, outcome=NULL, algo="rf", ncores=2)

vi05<- getVariableImportance(ml0, thr=0.5, verbose=TRUE)
table(E(vi05$dag)$color)

SEMdeep documentation built on April 12, 2025, 2:24 a.m.