\dontrun{} example wrappers replaced with
\donttest{}. The examples are real, runnable code; they
were wrapped only because some fit non-trivial GAMs that may exceed
CRAN’s 5-second budget, not because they cannot be executed.plot_partial() example is now self-contained (the
previous example referenced model objects that were never defined inside
the example block).New exported function plot_curve(). Builds a ggplot
of the partial effect of a single continuous predictor (with an optional
SE ribbon), or one continuous + one categorical predictor (one line per
factor level, coloured by level). Smooth/line counterpart to
plot_contour(). Named plot_curve to avoid
colliding with itsadug::plot_smooth.
New exported function plot_partial(). Convenience
dispatcher that picks plot_contour (for two numeric
view variables) or plot_curve (for one
numeric, or one numeric + one factor). Arguments specific to one
underlying function (e.g. too.far, zlim from
plot_contour) are silently dropped when dispatching to the
other, so the same call site can be reused across model shapes.
add_fit() now correctly retains the parametric main
effect of the by-variable (e.g. f in
~ f + s(x, by=f)) when terms.size = "medium"
or "max". Previously, the by-variable expansion logic also
caught the parametric main effect by string matching and rewrote it into
per-level names (fA, fB, …) that do not exist
as columns of predict.gam(type="terms"). mgcv silently
dropped them and the fit was missing the main-effect intercept shift
between levels. The new joint.se path was not affected; with the fix,
both paths now return identical fits.
New argument joint.se (default FALSE)
in add_fit() and plot_contour(). With
TRUE, the standard error of the summed partial effect is
computed via the lpmatrix and full vcov(mdl) rather than as
the square root of the sum of per-term variances. This gives the correct
joint SE when the selected terms are correlated (e.g., a parametric main
effect summed with a :-interaction, where cross-covariances
are typically negative). Default behavior is unchanged.
New argument too.far (default NULL) in
plot_contour(). When non-NULL, grid cells whose nearest
data point is farther than too.far (Euclidean distance
after each axis is rescaled to [0, 1] via the data’s
min/max) are masked out by setting fit, se,
lwr, upr to NA. This prevents the
plot from showing extrapolated regions the model has no support for.
Mirrors the too.far argument of mgcv::vis.gam
and itsadug::fvisgam.
Minimum R version bumped to 3.5.
add_fit() can now handle parametric
:-interactions (e.g., y ~ fac + x + fac:x).
Previously the term-selection helper find.pos() did not
split fac:x into its component variables, so the
interaction was silently excluded. With this fix,
terms.size = "min" selects the interaction term itself,
while "medium" and "max" include it together
with the matching main effects.
New argument include.parametric (default
TRUE) in add_fit() and
plot_contour(). With FALSE, only smooth terms
are eligible for the partial-effect computation; all parametric terms
(main effects and :-interactions) are dropped. Useful for
visualizing the smooth-only contribution in models that mix parametric
and smooth predictors.
add_fit() now correctly parses formulas that R’s
deparser has line-wrapped (RHS over ~500 chars). Previously the inserted
\n whitespace inside terms like
s(x, by = f, \n k = 3) broke the term-selection regexes,
causing affected smooths to be silently dropped from the partial-effect
computation.
In this version, you can… 1. give a data.table to the argument “ndat” of ndat_to_contour. 2. use the column name “type” for the argument “facet.col” of ndat_to_contour.