car (version 2.0-22)

mcPlots: Draw Linear Model Marginal and Conditional Plots in Parallel or Overlaid

Description

These functions take a fitted lm object and draw two plots for each regressor, one showing the marginal relationship between the response and the regressor, centered but otherwise ignoring all other regressors, and the other an added-variable plot, showing the conditional relationship of Y and X given all other regressors. The added variable plot by default is drawn using the same xlim and ylim as the centered marginal plot to emphasize that conditioning removes variation in both the regressor and the response.

Usage

mcPlots(model, terms=~., layout=NULL, ask, overlaid=TRUE, ...)

mcPlot(model, ...) 

## S3 method for class 'lm':
mcPlot(model, variable,
                      id.method = list(abs(residuals(model, type="pearson")), "x"),
                      labels, 
                      id.n = if(id.method[1]=="identify") Inf else 0,
                      id.cex=1, id.col=palette()[1],
                      col.marginal="blue", col.conditional="red", col.arrows="gray",
                      pch = c(16, 1), lwd = 2, grid=TRUE,   ###removed arg main
                      ellipse=FALSE, ellipse.args=list(levels=0.5), 
                      overlaid=TRUE, new=TRUE, ...)

Arguments

model
model object produced by lm or glm.
terms
A one-sided formula that specifies a subset of the predictors. One added-variable plot is drawn for each term. For example, the specification terms = ~.-X3 would plot against all terms except for X3. If this argument is a q
variable
A quoted string giving the name of a regressor in the model matrix for the horizontal axis
layout
If set to a value like c(1, 2) or c(6, 2), the layout of the graph will have this many rows and columns. If not set, the program will select an appropriate layout of up to c(4,2); with more than 4 regressors, graphs will be d
ask
If TRUE, ask the user before drawing the next plot; if FALSE don't ask.
...
mcPlots passes these arguments to mcPlot. mcPlot passes arguments to plot.
id.method,labels,id.n,id.cex,id.col
Arguments for the labelling of points. The default is id.n=0 for labeling no points. See showLabels for details of these arguments.
overlaid
If TRUE, overlay the marginal and conditional plots; otherwise plot them side-by-side
col.marginal, col.conditional
colors for points, lines, ellipses in the marginal and conditional plots, respectively
col.arrows
color for the arrows with overlaid=TRUE
pch
Plotting character for marginal and conditional plots, respectively.
lwd
line width; default is 2 (see par).
grid
If TRUE, the default, a light-gray background grid is put on the graph.
ellipse
If TRUE, plot a concentration ellipse; default is FALSE.
ellipse.args
Arguments to pass to the link{dataEllipse} function, in the form of a list with named elements; e.g., ellipse.args=list(robust=TRUE)) will cause the ellipse to be plotted using a robust covariance-matrix.
new
if TRUE, the default, the plot window is reset when overlaid=FALSE using par{mfrow=c(1, 2)}. If FALSE, the layout of the plot window is not reset. Users will ordinarily ignore this argument.

Value

  • These functions are used for their side effect id producing plots.

Details

With an lm object, suppose the response is Y, X is a focal regressor of interest, and Z is all the remaining regressors. This function produces two graphs. The marginal plot is simply of Y versus X, with each variable centered around its mean. The conditional plot is the added-variable plot of e(Y|Z) versus e(X|Z) where e(a|b) means the ordinary residuals from the regression of a on b. If overlaid=TRUE, these two plots are overlaid in one graph, with the points in different colors. In addition, each point in the marginal plot is joined to its value in the conditional plot by an arrow. Least squares regression lines fit to the marginal and conditional graphs are also shown; data ellipsoids can also be added. If overlaid=FALSE, then the two graphs are shown in side-by-side plots, and the arrows are omitted. These graphs are primarily for teaching, as the marginal plot shows the relationship between Y and X ignoring Z, while the conditional is the relationship between Y and X given X. By keeping the scales the same in both graphs the effect of conditioning on both X and Y can be visualized. These graphs are closely related to the ARES plots proposed by Cook and Weisberg (1989). This plot would benefit from animation.

References

Cook, R. D. and Weisberg, S. (1989) Regression diagnostics with dynamic graphics, Technometrics, 31, 277. Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models, Second Edition. Sage. Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage. Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley.

See Also

avPlots, residualPlots, crPlots, ceresPlots, dataEllipse

Examples

Run this code
m1 <- lm(partic ~ tfr + menwage + womwage + debt + parttime, data = Bfox)
mcPlot(m1, "womwage")
mcPlot(m1, "womwage", overlaid=FALSE, ellipse=TRUE)

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