regr1.plot

0th

Percentile

plot x and y, with optional straight line fit and display of squared residuals

plot x and y, with optional straight line fit and display of squared residuals

Keywords
models, regression
Usage
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)
Arguments
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
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
Note

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".

References

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.

See Also

resid.squares

Aliases
  • regr1.plot
Examples
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))
Documentation reproduced from package HH, version 1.5, License: GPL version 2 or newer

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