# lrPlot: Regression LR Influence Plot In mvinfluence: Influence Measures and Diagnostic Plots for Multivariate Linear Models

## Description

This function creates a “bubble” plot of functions, R = log(Studentized residuals^2) by L = log(H/p*(1-H)) of the hat values, with the areas of the circles representing the observations proportional to Cook's distances.

This plot, suggested by McCulloch & Meeter (1983) has the attractive property that contours of equal Cook's distance are diagnonal lines with slope = -1. Various reference lines are drawn on the plot corresponding to twice and three times the average hat value, a “large” squared studentized residual and contours of Cook's distance.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```lrPlot(model, ...) ## S3 method for class 'lm' lrPlot(model, scale = 12, xlab = "log Leverage factor [log H/p*(1-H)]", ylab = "log (Studentized Residual^2)", xlim = NULL, ylim, labels, id.method = "noteworthy", id.n = if (id.method[1] == "identify") Inf else 0, id.cex = 1, id.col = palette()[1], ref = c("h", "v", "d", "c"), ref.col = "gray", ref.lty = 2, ref.lab = TRUE, ...) ```

## Arguments

 `model` a linear or generalized-linear model. `scale` a factor to adjust the radii of the circles, in relation to `sqrt(CookD)` `xlab, ylab` axis labels. `xlim, ylim` Limits for x and y axes. In the space of (L, R) very small residuals typically extend the y axis enough to swamp the large residuals, so the default for `ylim` is set to a range of 6 log units starting at the maximum value. `labels, id.method, id.n, id.cex, id.col` settings for labelling points; see `link{showLabels}` for details. To omit point labelling, set `id.n=0`, the default. The default `id.method="noteworthy"` is used in this function to indicate setting labels for points with large Studentized residuals, hat-values or Cook's distances. See Details below. Set `id.method="identify"` for interactive point identification. `ref` Options to draw reference lines, any one or more of `c("h", "v", "d", "c")`. `"h"` and `"v"` draw horizontal and vertical reference lines at noteworthy values of R and L respectively. `"d"` draws equally spaced diagonal reference lines for contours of equal CookD. `"c"` draws diagonal reference lines corresponding to approximate 0.95 and 0.99 contours of CookD. `ref.col, ref.lty` Color and line type for reference lines. Reference lines for `"c" %in% ref` are handled separately. `ref.lab` A logical, indicating whether the reference lines should be labeled. `...` arguments to pass to the `plot` and `points` functions.

## Details

The `id.method="noteworthy"` setting also requires setting `id.n>0` to have any effect. Using `id.method="noteworthy"`, and `id.n>0`, the number of points labeled is the union of the largest `id.n` values on each of L, R, and CookD.

## Value

If points are identified, returns a data frame with the hat values, Studentized residuals and Cook's distance of the identified points. If no points are identified, nothing is returned. This function is primarily used for its side-effect of drawing a plot.

Michael Friendly

## References

A. J. Lawrence (1995). Deletion Influence and Masking in Regression Journal of the Royal Statistical Society. Series B (Methodological) , Vol. 57, No. 1, pp. 181-189.

McCulloch, C. E. & Meeter, D. (1983). Discussion of "Outliers..." by R. J. Beckman and R. D. Cook. Technometrics, 25, 152-155.

`influencePlot.mlm`
`influencePlot` in the `car` package for other methods
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```# artificial example from Lawrence (1995) x <- c( 0, 0, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 18, 18 ) y <- c( 0, 6, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 7, 18 ) DF <- data.frame(x,y, row.names=LETTERS[1:length(x)]) DF with(DF, { plot(x,y, pch=16, cex=1.3) abline(lm(y~x), col="red", lwd=2) NB <- c(1,2,13,14) text(x[NB],y[NB], LETTERS[NB], pos=c(4,4,2,2)) } ) mod <- lm(y~x, data=DF) # standard influence plot from car influencePlot(mod, id.n=4) # lrPlot version lrPlot(mod, id.n=4) library(car) dmod <- lm(prestige ~ income + education, data = Duncan) influencePlot(dmod, id.n=3) lrPlot(dmod, id.n=3) ```