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, groups, key=TRUE, ...)
marginalModelPlot(...)
## S3 method for class 'lm':
mmp(model, variable, sd = FALSE,
xlab = deparse(substitute(variable)),
smoother = loessLine, smoother.args=list(span=2/3),
key=TRUE, pch, groups=NULL, ...)
## S3 method for class 'default':
mmp(model, variable, sd = FALSE,
xlab = deparse(substitute(variable)), smoother=loessLine,
smoother.args, key=TRUE, pch, groups=NULL,
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, sd = FALSE,
xlab = deparse(substitute(variable)), smoother=gamLine,
smoother.args=list(k=3), key=TRUE, pch, groups=NULL,
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, ...)lm or glm,
for which there is a predict method defined.~ ., which specifies that all the terms in
formula(object) will be usedTRUE, then a marginal model plot in the direction
of the fitted values or linear predictor of a generalized linear model will
be drawn.c(1, 1) or c(4, 3), the layout
of the graph will have this many rows and columns. If not set, the program
will select an appropriate layout. If the number of graphs exceed nine, you
must select the laTRUE, ask before clearing the graph window to draw more plots.main="" to suppress the title;
if missing, a title will be supplied.mmps to mmp and
then to plot. Users should generally use mmps, or equivalently
marginalModelPlots.predict(object). Can be any other
vector of length equal to the number of observations in the object. Thus the
mmp function can be 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.ScatterplotSmoothers. For linear models and
the default method, the default smoother is the function
is the functioScatterplotSmoothers.terms argument.TRUE, include a key at the top of the plot, if FALSE omit the
key. If grouping is present, the key is only printed for the upper-left plot.id.n=0 suppresses labelling, and setting this
argument greater than zero will include labelling. See
showLabels for these arguments.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. 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 non-missing
value of c.
The smoothers used were changed in September 2012. For linear models, the
default smoother is still loess with the same smoothing parameters as were
used in the past, but these can be changed with the argument smoother.args.
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.ScatterplotSmoothers, plotc1 <- 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)
# 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)Run the code above in your browser using DataLab