linloess_plot {stevemisc} | R Documentation |
Compare Linear Smoother to LOESS Smoother for Your OLS Model
Description
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.
Usage
linloess_plot(
mod,
resid = TRUE,
smoother = "loess",
se = TRUE,
span = 0.75,
...
)
Arguments
mod |
a fitted OLS model |
resid |
logical, defaults to |
smoother |
defaults to "loess", and is passed to the 'method' argument for the non-linear smoother. |
se |
logical, defaults to |
span |
a numeric, defaults to .75. An adjustment to the smoother. Higher values permit smoother lines and might be warranted in the presence of sparse pockets of the data. |
... |
optional parameters, passed to the scatterplot
( |
Details
This function makes an implicit assumption that there is no variable in the regression formula with the name ".y" or ".resid".
It may be in your interest (for the sake of rudimentary diagnostic checks) to disable the standard error bands for particularly ill-fitting linear models.
Value
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.
Author(s)
Steven V. Miller
Examples
M1 <- lm(mpg ~ ., data=mtcars)
linloess_plot(M1)
linloess_plot(M1, color="black", pch=21)