HH (version 2.1-23)

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

Description

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.

Usage

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)

Arguments

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

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.

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))