regr1.plot {HH} | R Documentation |
Plot x
and y
,
with optional fitted line and display of squared residuals.
By default the least squares line is calculated and used.
Any other straight line
can be specified by placing its coefficients in coef.model
.
Any other fitted model can be calculated by specifying the model
argument.
Any other function of one variable can be specified in the
alt.function
argument. At most one of the arguments
model
, coef.model
, alt.function
can be specified.
regr1.plot(x, y, model=lm(y~x), coef.model, alt.function, main="put a useful title here", xlab=deparse(substitute(x)), ylab=deparse(substitute(y)), jitter.x=FALSE, resid.plot=FALSE, points.yhat=TRUE, pch=16, ..., length.x.set=51, x.name, pch.yhat=16, cex.yhat=par()$cex*.7, err=-1)
x |
x variable |
y |
y variable |
model |
Defaults to the simple linear model lm(y ~ x) .
Any 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 intercept and slope coefficients for a straight
line (for example, c(3,5) ) can be entered to illustrate
the sense in which they are not "least squares". |
alt.function |
Any function of a single argument can be placed
here. For example, alt.function=function(x) {3 + 2*x + 3*x^2} .
All coefficients must be specified. |
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. |
x.name |
If the model argument used a different name for
the independent variable, you might need to specify it. |
pch |
Plotting character for the observed points. |
pch.yhat |
Plotting character for the fitted points. |
cex.yhat |
cex for the fitted points. |
err |
Thedefault -1 suppresses warnings about out of bound
points. |
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) ## quadratic 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))