| regr1.plot {HH} | R Documentation |
plot x and y, with optional straight line fit and display of squared residuals
regr1.plot(x, y, model=lm(y~x), coef.model=coef(model),
main="put a useful title here",
xlab=deparse(substitute(x)),
ylab=deparse(substitute(y)),
jitter.x=FALSE,
resid.plot=FALSE,
points.yhat=TRUE,
..., length.x.set=51,
err=-1)
x |
x variable |
y |
y variable |
model |
Defaults to the simple linear model lm(y ~ x).
Any linear model object with one x
variable, such as the quadratic lm(y ~ x + I(x^2)) can be used. |
coef.model |
Defaults to the coefficients of the model
argument. Other coefficients can be entered to illustrate the
sense in which they are not "least squares".
|
main, xlab, ylab |
arguments to plot. |
jitter.x |
logical. If TRUE, the x is jittered before
plotting. Jittering is often helpful when there are multiple
y-values at the same level of x. |
resid.plot |
If FALSE, then do not plot the residuals.
If "square", then call resid.squares to plot the
squared residuals. If TRUE (or anything else),
then call resid.squares to plot
straight lines for the residuals. |
points.yhat |
logical. If TRUE, the predicted values
are plotted. |
... |
other arguments. |
length.x.set |
number of points used to plot the predicted values. |
err |
This plot is designed as a pedagogical example for introductory courses.
When resid.plot=="square", then we actually see the set of squares
for which the sum of their areas is minimized by the method of "least squares".
Richard M. Heiberger <rmh@temple.edu>
Heiberger, Richard~M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5.
Smith, W. and Gonick, L. (1993). The Cartoon Guide to Statistics. HarperCollins.
hardness <- read.table(hh("datasets/hardness.dat"), header=TRUE)
## linear and quadratic regressions
hardness.lin.lm <- lm(hardness ~ density, data=hardness)
hardness.quad.lm <- lm(hardness ~ density + I(density^2), data=hardness)
anova(hardness.quad.lm) ## qyadratic term has very low p-value
par(mfrow=c(1,2))
regr1.plot(hardness$density, hardness$hardness,
resid.plot="square",
main="squared residuals for linear fit",
xlab="density", ylab="hardness",
points.yhat=FALSE,
xlim=c(20,95), ylim=c(0,3400))
regr1.plot(hardness$density, hardness$hardness,
model=hardness.quad.lm,
resid.plot="square",
main="squared residuals for quadratic fit",
xlab="density", ylab="hardness",
points.yhat=FALSE,
xlim=c(20,95), ylim=c(0,3400))
par(mfrow=c(1,1))