Added variable plot provides information about the marginal importance of a predictor variable, given the other predictor variables already in the model. It shows the marginal importance of the variable in reducing the residual variability.
ols_plot_added_variable(model, print_plot = TRUE)An object of class lm.
logical; if TRUE, prints the plot else returns a plot object.
The added variable plot was introduced by Mosteller and Tukey (1977). It enables us to visualize the regression coefficient of a new variable being considered to be included in a model. The plot can be constructed for each predictor variable.
Let us assume we want to test the effect of adding/removing variable X from a model. Let the response variable of the model be Y
Steps to construct an added variable plot:
Regress Y on all variables other than X and store the residuals (Y residuals).
Regress X on all the other variables included in the model (X residuals).
Construct a scatter plot of Y residuals and X residuals.
What do the Y and X residuals represent? The Y residuals represent the part of Y not explained by all the variables other than X. The X residuals represent the part of X not explained by other variables. The slope of the line fitted to the points in the added variable plot is equal to the regression coefficient when Y is regressed on all variables including X.
A strong linear relationship in the added variable plot indicates the increased importance of the contribution of X to the model already containing the other predictors.
Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.
Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.
ols_plot_resid_regressor(), ols_plot_comp_plus_resid()
model <- lm(mpg ~ disp + hp + wt, data = mtcars)
ols_plot_added_variable(model)
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