# regr1.plot

From HH v3.1-42
0th

Percentile

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

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.

Keywords
models, regression
##### 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 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 The default -1 suppresses warnings about out of bound points. ##### 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 (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://www.springer.com/us/book/9781493921218 Smith, W. and Gonick, L. (1993). The Cartoon Guide to Statistics. HarperCollins. ##### See Also resid.squares ##### Aliases • regr1.plot ##### Examples # NOT RUN { data(hardness) ## 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,
resid.plot="square",