HH (version 1.8)

regr1.plot: plot x and y, with optional straight line fit and display of squared residuals

Description

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

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

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

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)  ## 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))

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