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Creates an interaction plot of EMMs based on a fitted model and a simple formula specification.
emmip(object, formula, ...)# S3 method for default
emmip(object, formula, type, CIs = FALSE, PIs = FALSE,
style, engine = get_emm_option("graphics.engine"), plotit = TRUE,
nesting.order = FALSE, ...)
emmip_ggplot(emms, style = "factor", dodge = 0.1, xlab = labs$xlab,
ylab = labs$ylab, tlab = labs$tlab, facetlab = "label_context", scale,
dotarg = list(shape = "circle"), linearg = list(linetype = "solid"),
CIarg = list(lwd = 2, alpha = 0.5), PIarg = list(lwd = 1.25, alpha =
0.33), col, ...)
emmip_lattice(emms, style = "factor", xlab = labs$xlab, ylab = labs$ylab,
tlab = labs$tlab, pch = c(1, 2, 6, 7, 9, 10, 15:20), lty = 1,
col = NULL, ...)
If plotit = FALSE, a data.frame (actually, a
summary_emm object) with the table of EMMs that would be plotted.
The variables plotted are named xvar and yvar, and the trace
factor is named tvar. This data frame has an added "labs"
attribute containing the labels xlab, ylab, and tlab
for these respective variables. The confidence limits are also
included, renamed LCL and UCL.
If plotit = TRUE, the function
returns an object of class "ggplot" or a "trellis", depending
on engine.
An object of class emmGrid, or a fitted model of a class
supported by the emmeans package
Formula of the form
trace.factors ~ x.factors | by.factors. The EMMs are
plotted against x.factor for each level of trace.factors.
by.factors is optional, but if present, it determines separate
panels. Each element of this formula may be a single factor in the model,
or a combination of factors using the * operator.
Additional arguments passed to emmeans (when
object is not already an emmGrid object),
predict.emmGrid,
emmip_ggplot, or emmip_lattice.
As in predict.emmGrid, this determines
whether we want to inverse-transform the predictions
(type = "response") or not (any other choice). The default is
"link", unless the "predict.type" option is in force; see
emm_options.
In addition, the user may specify type = "scale" to create a
transformed scale for the vertical axis based on object's response
transformation or link function.
Logical value. If TRUE, confidence intervals (or HPD intervals
for Bayesian models) are added to the plot
(works only with engine = "ggplot").
Logical value. If TRUE, prediction intervals are added to the plot
(works only with engine = "ggplot"). This is allowed only if the
underlying model family is "gaussian".
If both CIs and
PIs are TRUE, the prediction intervals will be somewhat
longer, lighter, and thinner than the confidence intervals. Additional
parameters to predict.emmGrid (e.g., sigma) may be passed via
.... For Bayesian models, PIs require frequentist = TRUE and
a value for sigma.
Optional character value. This has an effect only when the
horizontal variable is a single numeric variable. If style is
unspecified or "numeric", the horizontal scale will be numeric and
curves are plotted using lines (and no symbols). With style =
"factor", the horizontal variable is treated as the levels of a factor
(equally spaced along the horizontal scale), and curves are plotted using
lines and symbols. When the horizontal variable is character or factor, or
a combination of more than one predictor, "factor" style is always used.
Character value matching "ggplot" (default),
"lattice", or "none". The graphics engine to be used to produce the plot.
These require, respectively, the ggplot2 or lattice package to
be installed. Specifying "none" is equivalent to setting plotit = FALSE.
Logical value. If TRUE, a graphical object is returned;
if FALSE, a data.frame is returned containing all the values
used to construct the plot.
Logical value. If TRUE, factors that are nested
are presented in order according to their nesting factors, even if those nesting
factors are not present in formula. If FALSE, only the
variables in formula are used to order the variables.
A data.frame created by calling emmip with
plotit = FALSE. Certain variables and attributes are expected
to exist in this data frame; see the section detailing the rendering functions.
Numerical amount passed to ggplot2::position_dodge
by which points and intervals are offset so they do not collide.
Character labels for the horizontal axis, vertical
axis, and traces (the different curves), respectively. The emmip
function generates these automatically and provides therm via the labs
attribute, but the user may override these if desired.
Labeller for facets (when by variables are in play).
Use "label_value" to show just the factor levels, or "label_both"
to show both the factor names and factor levels. The default of
"label_context" decides which based on how many by factors there are.
See the documentation for ggplot2::label_context.
If not missing, an object of class scales::trans specifying
a (usually) nonlinear scaling for the vertical axis. For example,
scales = scales::log_trans() specifies a logarithmic scale. For
fine-tuning purposes, additional
arguments to ggplot2::scale_y_continuous may be included in ... .
list
of arguments passed to geom_point to customize appearance of points
list
of arguments passed to geom_line to customize appearance of lines
lists
of arguments passed to geom_linerange to customize appearance of intervals.
(Note: the linetype aesthetic defaults to "solid" under the hood)
With emmip_ggplot, this adds color = col (not
colour) to all of the *arg lists. This is intended for setting a
common color for everything, such as a black-and-white plot.
With emmip_lattice, col specifies the colors to use
for each group, recycled as needed. If not specified, the default trellis
colors are used.
(Lattice only) The plotting characters to use for each group (i.e., levels of
trace.factors). They are recycled as needed.
(Lattice only) The line types to use for each group. Recycled as needed.
The functions emmip_ggplot and emmip_lattice
are called when plotit == TRUE to render the plots;
but they may also be called later on an object saved via plotit = FALSE
(or engine = "none"). The functions require that emms contains variables
xvar, yvar, and tvar, and attributes "labs" and "vars".
Confidence intervals are plotted if variables LCL and UCL exist;
and prediction intervals are plotted if LPL and UPL exist.
Finally, it must contain the variables named in attr(emms, "vars").
In emmip_ggplot, colors, linetypes, and shapes are all assigned to
groups (according to tvar) unless overridden. So, for example, one may
have different symbols for each group by simply specifying dotarg = list().
If object is a fitted model, emmeans is called with an
appropriate specification to obtain estimated marginal means for each
combination of the factors present in formula (in addition, any
arguments in ... that match at, trend,
cov.reduce, or fac.reduce are passed to emmeans).
Otherwise, if object is an emmGrid object, its first element is
used, and it must contain one estimate for each combination of the factors
present in formula.
emmeans, interaction.plot.
#--- Three-factor example
noise.lm = lm(noise ~ size * type * side, data = auto.noise)
# Separate interaction plots of size by type, for each side
emmip(noise.lm, type ~ size | side)
# One interaction plot, using combinations of size and side as the x factor
# ... with added confidence intervals and some formatting changes
emmip(noise.lm, type ~ side * size, CIs = TRUE,
CIarg = list(lwd = 1, alpha = 1, color = "cyan"),
dotarg = list(color = "black"))
# Create a black-and-white version of above with different linetypes
# (Let the linetypes and symbols default to the palette)
emmip(noise.lm, type ~ side * size, CIs = TRUE, col = "black",
linearg = list(), dotarg = list(size = 2), CIarg = list(alpha = 1)) +
ggplot2::theme_bw()
# One interaction plot using combinations of type and side as the trace factor
emmip(noise.lm, type * side ~ size)
# Individual traces in panels
emmip(noise.lm, ~ size | type * side)
# Example for the 'style' argument
fib.lm = lm(strength ~ machine * sqrt(diameter), data = fiber)
fib.rg = ref_grid(fib.lm, at = list(diameter = c(3.5, 4, 4.5, 5, 5.5, 6)^2))
emmip(fib.rg, machine ~ diameter) # curves (because diameter is numeric)
emmip(fib.rg, machine ~ diameter, style = "factor") # points and lines
# For an example using extra ggplot2 code, see 'vignette("messy-data")',
# in the section on nested models.
### Options with transformations or link functions
neuralgia.glm <- glm(Pain ~ Treatment * Sex + Age, family = binomial(),
data = neuralgia)
# On link scale:
emmip(neuralgia.glm, Treatment ~ Sex)
# On response scale:
emmip(neuralgia.glm, Treatment ~ Sex, type = "response")
# With transformed axis scale and custom scale divisions
emmip(neuralgia.glm, Treatment ~ Sex, type = "scale",
breaks = seq(0.10, 0.90, by = 0.10))
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