mcPlots {car}  R Documentation 
the mcPlot
function draws two graphs or overlays the two graphs. For a response Y and a regressor X, the first plot is the marginal plot of Y versus X with both variables centered, visualizing the conditional distribution of Y given X ignoring all other regressors. The second plot is an addedvariable for X after all other regressors, visualizing the conditional distribution of Y given X after adjusting for all other predictors. 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.The plot is primarily intended as a pedagogical tool for understanding coefficients in firstorder models.
mcPlots(model, ...)
## Default S3 method:
mcPlots(model, terms=~., layout=NULL, ask, overlaid=TRUE, ...)
mcPlot(model, ...)
## S3 method for class 'lm'
mcPlot(model, variable, id=FALSE,
col.marginal=carPalette()[2], col.conditional=carPalette()[3],
col.arrows="gray", pch = c(16,1), cex=par("cex"), pt.wts=FALSE,
lwd = 2, grid=TRUE, ellipse=FALSE, overlaid=TRUE, new=TRUE,
title=TRUE, ...)
## S3 method for class 'glm'
mcPlot(model, ...)
model 
model object produced by 
terms 
A onesided formula that specifies a subset of the predictors.
One addedvariable plot is drawn for each regressor and for each basis vector used to define a factor. For example, the
specification 
variable 
A quoted string giving the name of a numeric predictor in the model matrix for the horizontal
axis. To plot against a factor, you need to specify the full name of one of the indicator variables that define the factor. For example, for a factor called 
layout 
If set to a value like 
ask 
If 
... 

id 
controls point identification; if 
overlaid 
If TRUE, the default, overlay the marginal and conditional plots on the same graph; otherwise plot them sidebyside. See the details below 
col.marginal, col.conditional 
colors for points, lines,
ellipses in the marginal and conditional plots, respectively. The defaults are determined by the 
col.arrows 
color for the arrows with 
pch 
Plotting character for marginal and conditional plots, respectively. 
cex 
size of plotted points; default is taken from 
pt.wts 
if 
lwd 
line width; default is 
grid 
If 
ellipse 
Arguments to pass to the 
new 
if 
title 
If TRUE, the default, the standard main argument in plot is used to add a standard title to each plot. If FALSE no title is used. 
With an lm
object, suppose the response is Y, X is a numeric regressor of interest, and Z is all the remaining predictors, possibly including interactions and factors. This function produces two graphs. The first graph is the marginal plot of Y versus X, with each variable centered around its mean. The second conditional plot is the addedvariable plot of e(YZ) versus e(XZ) where e(ab) means the Pearson 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 sidebyside plots as long as the second argument to layout
is equal to 2
, or layout
is set by the function. The arrows are omitted if the graphs are not overlaid.
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.
This function is intended for firstorder models with numeric predictors only. For a factor, one (pair) of mcPlots will be produced for each of the dummy variables in the basis for the factor, and the resulting plots are not generally meaningful because they depend on parameterization. If the mean function includes interactions, then mcPlots for main effects may violate the hierarchy principle, and may also be of little interest. mcPlots for interactions of numerical predictors, however, can be useful.
These graphs are closely related to the ARES plots proposed by Cook and Weisberg (1989). This plot would benefit from animation.
These functions are used for their side effect of producing plots.
John Fox jfox@mcmaster.ca, Sanford Weisberg sandy@umn.edu
Cook, R. D. and Weisberg, S. (1989) Regression diagnostics with dynamic graphics, Technometrics, 31, 277.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley.
avPlots
, residualPlots
, crPlots
, ceresPlots
,
dataEllipse
m1 < lm(partic ~ tfr + menwage + womwage + debt + parttime, data = Bfox)
mcPlot(m1, "womwage")
mcPlot(m1, "womwage", overlaid=FALSE, ellipse=TRUE)