mcPlots | 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 added-variable 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 first-order 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 one-sided formula that specifies a subset of the predictors.
One added-variable 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 side-by-side. 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 added-variable plot of e(Y|Z) versus e(X|Z) where e(a|b) 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 side-by-side 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 first-order 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.