linloess_plot() provides a visual diagnostic of the linearity assumption of the OLS model.
Provided an OLS model fit by
lm() in base R, the function extracts the model frame and creates a faceted
scatterplot. For each facet, a linear smoother and LOESS smoother are estimated over the points. Users who run
this function can assess just how much the linear smoother and LOESS smoother diverge. The more they diverge, the
more the user can determine how much the OLS model is a good fit as specified. The plot will also point to potential
outliers that may need further consideration.
a fitted OLS model
optional parameters, passed to the scatterplot (
This function makes an implicit assumption that there is no variable in the regression formula with the name ".y".
linloess_plot() returns a faceted scatterplot as a ggplot2 object. The linear smoother is in solid blue (with blue
standard error bands) and the LOESS smoother is a dashed black line (with gray/default standard error bands). You can add
cosmetic features to it after the fact. The function may spit warnings to you related to the LOESS smoother, depending your data. I think
these to be fine the extent to which this is really just a visual aid and an informal diagnostic for the linearity assumption.
Steven V. Miller
M1 <- lm(mpg ~ ., data=mtcars) linloess_plot(M1) linloess_plot(M1, color="black", pch=21)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.