mmps {car} | R Documentation |
For a regression object, plots the response on the vertical axis versus
a linear combination u of terms in the mean function on the horizontal
axis. Added to the plot are a loess
smooth for the graph, along with
a loess 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, ...) marginalModelPlot(...) ## S3 method for class 'lm' mmp(model, variable, mean = TRUE, sd = FALSE, xlab = deparse(substitute(variable)), degree = 1, span = 2/3, key=TRUE, ...) ## Default S3 method: mmp(model, variable, mean = TRUE, sd = FALSE, xlab = deparse(substitute(variable)), degree = 1, span = 2/3, key = TRUE, col.line = palette()[c(4,2)], col=palette()[1], labels, id.method = "y", id.n=if(id.method[1]=="identify") Inf else 0, id.cex = 1, id.col=palette()[1], grid=TRUE, ...) ## S3 method for class 'glm' mmp(model, variable, mean = TRUE, sd = FALSE, xlab = deparse(substitute(variable)), degree = 1, span = 2/3, key=TRUE, col.line = palette()[c(4, 2)], col=palette()[1], labels, id.method="y", id.n=if(id.method[1]=="identify") Inf else 0, id.cex=1, id.col=palette()[1], grid=TRUE, ...)
model |
A regression object, usually of class either lm or glm ,
for which there is a predict method defined. |
terms |
A one-sided formula. A marginal model plot will be drawn for
each variable on the right-side of this formula that is not a factor. The
default is ~ . , which specifies that all the terms in
formula(object) will be used. See examples below. |
fitted |
If the default TRUE , then a marginal model plot in the direction
of the fitted values or linear predictor of a generalized linear model will
be drawn. |
layout |
A reasonable layout for the plots in the window is
determined by the program. If you don't like the default you can set your
own layout: c(2, 3) means two rows and three columns. |
ask |
If TRUE , ask before clearing the graph window to draw more plots. |
main |
Main title for the array of plots. Use main="" to suppress the title;
if missing, a title will be supplied.
|
... |
Additional arguments passed from mmps to mmp and
then to plot . Users should generally use mmps , or equivalently
marginalModelPlots .
|
variable |
The quantity to be plotted on the horizontal axis. The
default is the predicted values predict(object) . Can be any other
vector of length equal to the number of observations in the object. Thus the
mmp function can be used to get a marginal model plot versus any
predictor or term while the mmps function can be used only to get
marginal model plots for the first-order terms in the formula. In
particular, terms defined by a spline basis are skipped by mmps , but
you can use mmp to get the plot for the variable used to define
the splines. |
mean |
If TRUE , compare mean smooths |
sd |
If TRUE , compare sd smooths. For a binomial regression with all
sample sizes equal to one, this argument is ignored as the SD bounds don't
make any sense. |
xlab |
label for horizontal axis |
degree |
Degree of the local polynomial, passed to loess . The
usual default for loess is 2, but the default here is 1. |
span |
Span, the smoothing parameter for loess . |
key |
If TRUE , include a key at the top of the plot, if FALSE omit the
key |
id.method,labels,id.n,id.cex,id.col |
Arguments for labelling
points. The default id.n=0 suppresses labelling, and setting this
argument greater than zero will include labelling. See
showLabels for these arguments. |
col.line |
colors for data and model smooth, respectively. Using the default palette, these are blue and red. |
col |
color(s) for the plotted points. |
grid |
If TRUE, the default, a light-gray 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 predictors) 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 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.
Used for its side effect of producing plots.
Sanford Weisberg, sandy@stat.umn.edu
Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition. Sage.
Weisberg, S. (2005) Applied Linear Regression, Third Edition, Wiley, Chapter 8.
## Not run: c1 <- lm(infant.mortality ~ gdp, UN) mmps(c1) c2 <- update(c1, ~ poly(gdp, 4), data=na.omit(UN)) # plot against predict(c2, type="terms")[, "poly(gdp, 4)"] and # and against gdp mmps(c2, ~ poly(gdp,4) + gdp) # include SD lines p1 <- lm(prestige ~ income + education, Prestige) mmps(p1, sd=TRUE) # logisitic regression example # smoothers return warning messages. m1 <- glm(lfp ~ ., family=binomial, data=Mroz) mmps(m1) ## End(Not run)