mmps {car}  R Documentation 
For a regression object, draw a plot of the response on the vertical axis versus
a linear combination u of regressors in the mean function on the horizontal
axis. Added to the plot are a smooth for the graph, along with
a smooth from the plot of the fitted values on u. mmps
is an alias
for marginalModelPlots
, and mmp
is an alias for marginalModelPlot
.
marginalModelPlots(...) mmps(model, terms= ~ ., fitted=TRUE, layout=NULL, ask, main, groups, key=TRUE, ...) marginalModelPlot(...) mmp(model, ...) ## S3 method for class 'lm' mmp(model, variable, sd = FALSE, xlab = deparse(substitute(variable)), smooth=TRUE, key=TRUE, pch, groups=NULL, ...) ## Default S3 method: mmp(model, variable, sd = FALSE, xlab = deparse(substitute(variable)), ylab, smooth=TRUE, key=TRUE, pch, groups=NULL, col.line = carPalette()[c(2, 8)], col=carPalette()[1], id=FALSE, grid=TRUE, ...) ## S3 method for class 'glm' mmp(model, variable, sd = FALSE, xlab = deparse(substitute(variable)), ylab, smooth=TRUE, key=TRUE, pch, groups=NULL, col.line = carPalette()[c(2, 8)], col=carPalette()[1], id=FALSE, grid=TRUE, ...)
model 
A regression object, usually of class either 
terms 
A onesided formula. A marginal model plot will be drawn for
each term on the rightside of this formula that is not a factor. The
default is 
fitted 
If 
layout 
If set to a value like 
ask 
If 
main 
Main title for the array of plots. Use 
... 
Additional arguments passed from 
variable 
The quantity to be plotted on the horizontal axis. If this argument
is missing, the horizontal variable is the linear predictor, returned by

sd 
If 
xlab 
label for horizontal axis. 
ylab 
label for vertical axis, defaults to name of response. 
smooth 
specifies the smoother to be used along with its arguments; if 
groups 
The name of a vector that specifies a grouping variable for
separate colors/smoothers. This can also be specified as a conditioning
argument on the 
key 
If 
id 
controls point identification; if 
pch 
plotting character to use if no grouping is present. 
col.line 
colors for data and model smooth, respectively. The default is to use 
col 
color(s) for the plotted points. 
grid 
If TRUE, the default, a lightgray background grid is put on the graph 
mmp
and marginalModelPlot
draw one marginal model plot against
whatever is specified as the horizontal axis.
mmps
and marginalModelPlots
draws marginal model plots
versus each of the terms in the terms
argument and versus fitted values.
mmps
skips factors and interactions if they are specified in the
terms
argument. Terms based on polynomials or on splines (or
potentially any term that is represented by a matrix of regressors) will
be used to form a marginal model plot by returning a linear combination of the
terms. For example, if you specify terms = ~ X1 + poly(X2, 3)
and
poly(X2, 3)
was part of the original model formula, the horizontal
axis of the marginal model plot for X2
will be the value of
predict(model, type="terms")[, "poly(X2, 3)"])
. If the predict
method for the model you are using doesn't support type="terms"
,
then the polynomial/spline term is skipped. Adding a conditioning variable,
e.g., terms = ~ a + b  c
, will produce marginal model plots for a
and b
with different colors and smoothers for each unique nonmissing
value of c
.
For linear models, the default smoother is loess.
For generalized linear models, the default smoother uses gamLine
, fitting
a generalized additive model with the same family, link and weights as the fit of the
model. SD smooths are not computed for for generalized linear models.
For generalized linear models the default number of elements in the spline basis is
k=3
; this is done to allow fitting for predictors with just a few support
points. If you have many support points you may wish to set k
to a higher
number, or k=1
for the default used by gam
.
Used for its side effect of producing plots.
Sanford Weisberg, sandy@umn.edu
Cook, R. D., & Weisberg, S. (1997). Graphics for assessing the adequacy of regression models. Journal of the American Statistical Association, 92(438), 490499.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition. Sage.
Weisberg, S. (2005) Applied Linear Regression, Third Edition, Wiley, Section 8.4.
c1 < lm(infantMortality ~ ppgdp, UN) mmps(c1) c2 < update(c1, ~ log(ppgdp)) mmps(c2) # include SD lines p1 < lm(prestige ~ income + education, Prestige) mmps(p1, sd=TRUE) # condition on type: mmps(p1, ~.  type) # logisitic regression example # smoothers return warning messages. # fit a separate smoother and color for each type of occupation. m1 < glm(lfp ~ ., family=binomial, data=Mroz) mmps(m1)