Description Usage Arguments Value References Examples
View source: R/ellipse-functions.R
Draws a contour of constant density at the (1-alpha
)100% level for a
bivariate normal distribution using the eigendecomposition of the covariance
matrix. This is likely more interesting for learning about the bivariate
normal distribution than as a practical tool, for which other functions
already exist (e.g. link[graphics]{contour}
).
1 2 3 4 5 6 7 8 9 10 11 12 |
mu |
a vector giving the mean of the bivariate normal distribution. This is the center of the ellipse. |
Sigma |
a matrix giving the covariance matrix of the bivariate normal
distribution. Either |
eig |
the eigenvalues and eigenvectors of the covariance matrix. This
should be of the same form as the output of |
xl |
a vector giving the lower and upper limits of the x-axis for
plotting. If |
yl |
a vector giving the lower and upper limits of the y-axis for
plotting. If |
axes |
logical. If |
center |
logical. If |
lim.adj |
a value giving an adjustment to the x-axis and y-axis limits
computed if either |
alpha |
a value giving the value of alpha to be used when computing the
contour. Contours are drawn at the |
... |
other arguments to be passed to the graphing functions. |
None
Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed). Pearson Prentice Hall.
1 2 3 4 | mu <- c(-1,8)
Sigma <- matrix(c(3,2,2,4), ncol = 2)
# Draw a 90% contour
bvNormalContour(mu = mu, Sigma = Sigma, alpha = 0.10)
|
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