contour.lm {rsm} | R Documentation |
contour
method that will work with any lm
object involving two or
more numerical predictors.
## S3 method for class 'lm': contour(x, form, at, bounds, zlim, image = TRUE, img.col = terrain.colors(50), ...)
x |
A lm object. |
form |
A formula, or a list of formulas. |
at |
Optional named list of fixed values to use for variables other than the axis variables. If not provided, the mean is used for numeric variables, or the first level if it is a factor. |
bounds |
Optional named list of bounds or grid values to use for the variables having the same names. See details. |
zlim |
Optional zlim setting passed to image .
If not provided, the range of values across all plotted surfaces is used. |
image |
Set to TRUE if you want an image plot underneath the contours. |
img.col |
Color map to use when image=TRUE . |
... |
Additional arguments passed to contour . |
form
may be a single formula or a list of formulas. A simple formula like
x2 ~ x1
will produce a contour plot of the fitted regression surface
for combinations of x2
(vertical axis) and x1
(horizontal axis).
A list of several such simple formulas will produce a contour plot for each formula.
A two-sided formula produces contour plots for each left-hand variable versus each
right-hand variable (except when they are the same); for example,
x1+x3 ~ x2+x3
is equivalent to
list(x1~x2, x3~x2, x1~x3)
.
A one-sided formula produces contour plots for each pair of variables. For example,
~ x1+x2+x3
is equivalent to
list(x2~x1, x3~x1, x3~x2)
.
For any variables not in the bounds
argument, a grid of 26 equally-spaced
values in the observed range of that variable is used. If you specify a vector of
length 2, it is interpreted as the desired range for that variable and a grid of 26
equally-spaced points is generated. If it is a vector of length 3, the first two elements are used
as the range, and the third as the number of grid points.
If it is a vector of length 4 or more, those
values are used directly as the grid values.
Russell V. Lenth
library (rsm) heli.rsm = rsm (ave ~ block + SO(x1, x2, x3, x4), data = heli) par (mfrow = c (2,3)) contour (heli.rsm, ~x1+x2+x3+x4, at=summary(heli.rsm)$canonical$xs)