This function provides several plots of the result of fitting a two-predictor model.
# S3 method for lm plot3d(x, which = 1, plane.col = "gray", plane.alpha = 0.5, sharedMouse = TRUE, use_surface3d, do_grid = TRUE, grid.col = "black", grid.alpha = 1, grid.steps = 5, sub.steps = 4, vars = get_all_vars(terms(x), x$model), clip_to_density = 0, ...)
An object inheriting from class
"lm" obtained by fitting
a two-predictor model.
Which plot to show? See Details below.
These parameters control the colour and transparency of a plane or surface.
If multiple plots are requested, should they share mouse controls, so that they move in sync?
surface3d function to plot the surface
planes3d. This allows curved
surfaces to be shown. The default is
FALSE if the model
looks like a simple 2 parameter linear fit, otherwise
Plot a grid.
Characteristics of the grid.
use an internal grid of
grid.steps*sub.steps to draw
sub.steps > 1 allows curvature within facets.
TRUE, it allows
curvature within grid lines.
A dataframe containing the variables to plot in the first three columns, with the response assumed to be in column 1. See the Note below.
If positive, the surface, plane or grid will be clipped to a region with sufficient data.
Other parameters to pass to the default
plot3d method, to control the appearance of aspects of the plot
other than the plane.
Three plots are possible, depending on the value(s) in
(default) Show the points and the fitted plane or surface.
Show the residuals and the plane at
z = 0.
Show the predicted values on the fitted plane or surface.
clip_to_density is positive, then the
surface, plane or grid will be clipped to
the region where a non-parametric density estimate
MASS::kde2d), normalized to have a
maximum value of 1, is greater than the given value.
This will suppress parts of the plot that aren't
supported by the observed data.
Called for the side effect of drawing one or more plots.
Invisibly returns a high-level vector of object ids. Names of object ids have the plot number (in drawing order) appended.
The default value for the
vars argument will handle
simple linear models with a response and two predictors, and
some models with functions of those two predictors. For
models that fail (e.g. models using
can include the observed values as in the third example below.
clip_to_density > 0,
The clipping is approximate, so it may not agree perfectly between surfaces, planes and grids.
This option requires the suggested packages MASS and interp, and will be ignored with a warning if either is not installed.
open3d() ids <- plot3d(lm(mpg ~ wt + qsec, data = mtcars), which = 1:3) names(ids) #>  "data.1" "axes.1" "xlab.1" "ylab.1" "zlab.1" "plane.1" "grid.1" #>  "data.2" "axes.2" "xlab.2" "ylab.2" "zlab.2" "plane.2" "grid.2" #>  "data.3" "axes.3" "xlab.3" "ylab.3" "zlab.3" "plane.3" "grid.3" open3d() plot3d(lm(mpg ~ wt + I(wt^2) + qsec, data = mtcars))
3D plotopen3d() # Specify vars in the order: response, pred1, pred2. plot3d(lm(mpg ~ poly(wt, 3) + qsec, data = mtcars), vars = mtcars[,c("mpg", "wt", "qsec")])
3D plotopen3d() # Clip parts of the plot with few (wt, qsec) points plot3d(lm(mpg ~ poly(wt, 3) + qsec, data = mtcars), vars = mtcars[,c("mpg", "wt", "qsec")], clip_to_density = 0.1)