Make an ellipsoid
A generic function and several methods returning an ellipsoid or other outline of a confidence region for three parameters.
ellipse3d(x, ...) ## Default S3 method: ellipse3d(x, scale = c(1, 1, 1), centre = c(0, 0, 0), level = 0.95, t = sqrt(qchisq(level, 3)), which = 1:3, subdivide = 3, smooth = TRUE, ...) ## S3 method for class 'lm' ellipse3d(x, which = 1:3, level = 0.95, t = sqrt(3 * qf(level, 3, x$df.residual)), ...) ## S3 method for class 'glm' ellipse3d(x, which = 1:3, level = 0.95, t, dispersion, ...) ## S3 method for class 'nls' ellipse3d(x, which = 1:3, level = 0.95, t = sqrt(3 * qf(level, 3, s$df[2])), ...)
x |
An object. In the default method the parameter |
... |
Additional parameters to pass to the default method or to |
scale |
If |
centre |
The centre of the ellipse will be at this position. |
level |
The confidence level of a simultaneous confidence region. The default is 0.95, for a 95% region. This is used to control the size of the ellipsoid. |
t |
The size of the ellipse may also be controlled by specifying the value of a t-statistic on its boundary. This defaults to the appropriate value for the confidence region. |
which |
This parameter selects which variables from the object will be plotted. The default is the first 3. |
subdivide |
This controls the number of subdivisions (see |
smooth |
If |
dispersion |
The value of dispersion to use. If specified, it is treated as fixed,
and chi-square limits for |
A mesh3d
object representing the ellipsoid.
# Plot a random sample and an ellipsoid of concentration corresponding to a 95% # probability region for a # trivariate normal distribution with mean 0, unit variances and # correlation 0.8. if (requireNamespace("MASS", quietly = TRUE)) { Sigma <- matrix(c(10, 3, 0, 3, 2, 0, 0, 0, 1), 3, 3) Mean <- 1:3 x <- MASS::mvrnorm(1000, Mean, Sigma) open3d() plot3d(x, box = FALSE) plot3d( ellipse3d(Sigma, centre = Mean), col = "green", alpha = 0.5, add = TRUE) } # Plot the estimate and joint 90% confidence region for the displacement and cylinder # count linear coefficients in the mtcars dataset data(mtcars) fit <- lm(mpg ~ disp + cyl , mtcars) open3d() plot3d(ellipse3d(fit, level = 0.90), col = "blue", alpha = 0.5, aspect = TRUE)
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