ham (version 1.0.0)

importance: Importance of variables based on partial chi-square statistic

Description

Calculates partial chi-square (Wald chi-square for individual coefficients) from assess class objects. The importance is the partial chi-square minus its degrees of freedom based on the regression coefficients (Harrell, 2015). A higher chi-square indicates a larger effect by the predictors. Therefore, the rank of the chi-square can indicate which predictors can contribute more in explaining the variation in the outcome variable.

Usage

importance(model)

Value

a data.frame object with partial X^2 summary statistics.

Arguments

model

an assess class object or models with lm or glm class.

References

Harrell, F. E., Jr. (2016). Regression Modeling Strategies. Springer International Publishing. ISBN: 978-3-319-19424-0.

Examples

# OLS regression
importance(assess(mpg ~ hp + wt + cyl, data=mtcars, regression= "ols")$model)

# logistic regression
importance(assess(vs~mpg+wt+hp, data=mtcars, regression= "logistic")$model)