confint.fderiv() and confint.gam() now
return their results as a tibble instead of a common-or-garden data
frame. The latter mostly already did this.
Examples for confint.fderiv() and
confint.gam() were reworked, in part to remove some
inconsistent output in the examples when run on M1 macs.
compare_smooths() failed when passed non-standard model
“names” like compare_smooths(m_gam, m_gamm$gam) or
compare_smooths(l[[1]], l[[2]]) even if the evaluated
objects were valid GAM(M) models. Reported by Andrew Irwin #150draw.gam() and draw.smooth_estimates()
can now handle splines on the sphere
(s(lat, long, bs = "sos")) with special plotting methods
using ggplot2::coord_map() to handle the projection to
spherical coordinates. An orthographic projection is used by default,
with an essentially arbitrary (and northern hemisphere-centric) default
for the orientation of the view.
fitted_values() insures that data (and
hence the returned object) is a tibble rather than a common or garden
data frame.
draw.posterior_smooths() was redundantly plotting
duplicate data in the rug plot. Now only the unique set of covariate
values are used for drawing the rug.
data_sim() was not passing the scale
argument in the bivariate example setting ("eg2").
draw() methods for gamm() and
gamm4::gamm4() fits were not passing arguments on to
draw.gam().
draw.smooth_estimates() would produce a subtitle
with data for a continuous by smooth as if it were a factor by smooth.
Now the subtitle only contains the name of the continuous by
variable.
Due to an issue with the size of the package source tarball, which wasn’t discovered until after submission to CRAN, 0.7.1 was never released.
draw.gam() and draw.smooth_estimates():
{gratia} can now handle smooths of 3 or 4 covariates when plotting. For
smooths of 3 covariates, the third covariate is handled with
ggplot2::facet_wrap() and a set (default n =
16) of small multiples is drawn, each a 2d surface evaluated at the
specified value of the third covariate. For smooths of 4 covariates,
ggplot2::facet_grid() is used to draw the small multiples,
with the default producing 4 rows by 4 columns of plots at the specific
values of the third and fourth covariates. The number of small multiples
produced is controlled by new arguments n_3d (default =
n_3d = 16) and n_4d (default
n_4d = 4, yielding n_4d * n_4d = 16 facets)
respectively.
This only affects plotting; smooth_estimates() has been
able to handle smooths of any number of covariates for a while.
When handling higher-dimensional smooths, actually drawing the plots
on the default device can be slow, especially with the default value of
n = 100 (which for 3D or 4D smooths would result in 160,000
data points being plotted). As such it is recommended that you reduce
n to a smaller value: n = 50 is a reasonable
compromise of resolution and speed.
model_concurvity() returns concurvity measures from
mgcv::concurvity() for estimated GAMs in a tidy format. The
synonym concrvity() is also provided. A draw()
method is provided which produces a bar plot or a heatmap of the
concurvity values depending on whether the overall concurvity of each
smooth or the pairwise concurvity of each smooth in the model is
requested.
draw.gam() gains argument
resid_col = "steelblue3" that allows the colour of the
partial residuals (if plotted) to be changed.
model_edf() was not using the type
argument. As a result it only ever returned the default EDF
type.
add_constant() methods weren’t applying the constant
to all the required variables.
draw.gam(), draw.parametric_effects()
now actually work for a model with only parametric effects. #142
Reported by @Nelson-Gon
parametric_effects() would fail for a model with
only parametric terms because predict.gam() returns empty
arrays when passed exclude = character(0).
draw.gam() now uses smooth_estimates()
internally and consequently uses its draw() method and
underlying plotting code. This has simplified the code compared to
evaluate_smooth() and its methods, which will allow for
future development and addition of features more easily than if
evaluate_smooth() had been retained.
Similarly, evaluate_parametric_terms() is now deprecated
in favour of parametric_effects(), which is also used
internally by draw.gam() if parametric terms are present in
the model (and parametric = TRUE).
While a lot of code has been reused so differences between plots as a result of this change should be minimal, some corner cases may have been missed. File an Issue if you notice something that has changed that you think shouldn’t.
draw.gam() now plots 2D isotropic smooths (TPRS and
Duchon splines) with equally-scaled x and y coordinates using
coord_equal(ratio = 1). Alignment of these plots will be a
little different now when plotting models with multiple smooths. See
Issue #81.
From version 0.7.0, the following functions are considered deprecated and their use is discouraged:
fderiv() is soft-deprecated in favour of
derivatives(),evaluate_smooth() is soft-deprecated in favour
of smooth_estimates(),evaluate_parametric_term() is soft-deprecated
in favour of parametric_effects().The first call to one of these functions will generate a warning,
pointing to the newer, alternative, function. It is safe to ignore these
warnings, but these deprecated functions will no longer receive updates
and are thus at risk of being removed from the package at some future
date. The newer alternatives can handle more types of models and
smooths, especially so in the case of
smooth_estimates().
fitted_values() provides a tidy wrapper around
predict.gam() for generating fitted values from the model.
New covariate values can be provided via argument data. A
credible interval on the fitted values is returned, and values can be on
the link (linear predictor) or response scale.
Note that this function returns expected values of the response. Hence, “fitted values” is used instead of “predictions” in the case of new covariate values to differentiate these values from the case of generating new response values from a fitted model.
rootogram() and its draw() method
produce rootograms as diagnostic plots for fitted models. Currently only
for models fitted with poisson(), nb(),
negbin(), gaussian() families.
New helper functions typical_values(),
factor_combos() and data_combos() for quickly
creating data sets for producing predictions from fitted models where
some covariatess are fixed at come typical or representative values.
typical_values() is a new helper function to return
typical values for the covariates of a fitted model. It returns the
value of the observation closest to the median for numerical covariates
or the modal level of a factor while preserving the levels of that
factor. typical_values() is useful in preparing data slices
or scenarios for which fitted values from the estimated model are
required.
factor_combos() extracts and returns the combinations of
levels of factors found in data used to fit a model. Unlike
typical_values(), factor_combos() returns all
the combinations of factor levels observed in the data, not just the
modal level. Optionally, all combinations of factor levels can be
returned, not just those in the observed data.
data_combos() combines returns the factor data from
factor_combos() plus the typical values of numerical
covariates. This is useful if you want to generate predictions from the
model for each combination of factor terms while holding any continuous
covariates at their median values.
nb_theta() is a new extractor function that returns
the theta parameter of a fitted negative binomial GAM (families
nb() or negbin()). Additionally,
theta() and has_theta() provide additional
functionality. theta() is an experimental function for
extracting any additional parameters from the model or family.
has_theta() is useful for checking if any additional
parameters are available from the family or model.
edf() extracts the effective degrees of freedom
(EDF) of a fitted model or a specific smooth in the model. Various forms
for the EDF can be extracted.
model_edf() returns the EDF of the overall model. If
supplied with multiple models, the EDFs of each model are returned for
comparison.
draw.gam() can now show a “rug” plot on a bivariate
smooth by drawing small points with high transparency over the smooth
surface at the data coordinates.
In addition, the rugs on plots of factor by smooths now show the locations of covariate values for the specific level of the factor and not over all levels. This better reflects what data were used to estimate the smooth, even though the basis for each smooth was set up using all of the covariate locations.
draw.gam() and draw.smooth_estimates()
now allow some aspects of the plot to be changed: the fill (but not
colour) and alpha attributes of the credible interval, and the line
colour for the smooth can now be specified using arguments
ci_col, ci_alpha, and smooth_col
respectively.
Partial residuals can now be plotted on factor by smooths. To allow this, the partial residuals are filtered so that only residuals associated with a particular level’s smooth are drawn on the plot of the smooth.
smooth_estimates() uses
check_user_select_smooths() to handle user-specified
selection of smooth terms. As such it is more flexible than previously,
and allows for easier selection of smooths to evaluate.
fixef() is now imported (and re-exported) from the
nlme package, with methods for models fitted with
gam() and gamm(), to extract fixed effects
estimates from fitted models. fixed_effects() is an alias
for fixef().
The draw() method for smooth_samples()
can now handle 2D smooths. Additionally, the number of posterior draws
to plot can now be specified when plotting using new argument
n_samples, which will result in n_samples
draws being selected at random from the set of draws for plotting. New
argument seed allows the selection of draws to be
repeatable.
smooth_estimates() was not filtering user-supplied
data for the by level of the specific smooth when used with by factor
smooths. This would result in the smooth being evaluated at all rows of
the user-supplied data, and therefore would result in
nrow(user_data) * nlevels(by_variable) rows in the returned
object instead of nrow(user_data) rows.
The add_confint() method for
smooth_estimates() had the upper and lower intervals
reversed. #107 Reported by @Aariq
draw.gam() and smooth_estimates() were
both ignoring the dist argument that allows covariate
values that lie too far from the support of the data to be excluded when
returning estimated values from the smooth and plotting it. #111
Reported by @Aariq
smooth_samples() with a factor by GAM would return
samples for the first factor level only. Reported by @rroyaute in discussion of
#121
smooth_samples() would fail if the model contained
random effect “smooths”. These are now ignored with a message when
running smooth_samples(). Reported by @isabellaghement in
#121
link(), inv_link() were failing on
models fitted with family = scat(). Reported by @Aariq #130
The {cowplot} package has been replaced by the {patchwork}
package for producing multi-panel figures in draw() and
appraise(). This shouldn’t affect any code that used
{gratia} only, but if you passed additional arguments to
cowplot::plot_grid() or used the align or
axis arguments of draw() and
appraise(), you’ll need to adapt code accordingly.
Typically, you can simply delete the align or
axis arguments and {patchwork} will just work and align
plots nicely. Any arguments passed via ... to
cowplot::plot_grid() will just be ignored by
patchwork::wrap_plots() unless those passed arguments match
any of the arguments of patchwork::wrap_plots().
The {patchwork} package is now used for multi-panel figures. As such, {gratia} no longer Imports from the {cowplot} package.
Worm plot diagnostic plots are available via new function
worm_plot(). Worm plots are detrended Q-Q plots, where
deviation from the Q-Q reference line are emphasized as deviations
around the line occupy the full height of the plot.
worm_plot() methods are available for models of classes
"gam", "glm", and "lm".
(#62)
Smooths can now be compared across models using
compare_smooths(), and comparisons visualised with the
associated draw() method. (#85 @dill)
This feature is a bit experimental; the returned object uses nested lists and may change in the future if users find this confusing.
The reference line in qq_plot() with
method = "normal" was previously drawn as a line with
intercept 0 and slope 1, to match the other methods. This was
inconsistent with stats::qqplot() which drew the line
through the 1st and 3rd quartiles. qq_plot() with
method = "normal" now uses this robust reference line.
Reference lines for the other methods remain drawn with slope 1 and
intercept 0.
qq_plot() with method = "normal" now
draws a point-wise reference band using the standard error of the order
statistic.
The draw() method for penalty() now
plots the penalty matrix heatmaps in a more-logical orientation, to
match how the matrices might be written down or printed to the R
console.
link(), and inv_link() now work for
models fitted with the gumbls() and shash()
families. (#84)
extract_link() is a lower level utility function
related to link() and inv_link(), and is now
exported.
The default method name for generating reference quantiles in
qq_plot() was changed from "direct" to
"uniform", to avoid confusion with the
mgcv::qq.gam() help page description of the methods.
Accordingly using method = "direct" is deprecated and a
message to this effect is displayed if used.
The way smooths/terms are selected in derivatives()
has been switched to use the same mechanism as draw.gam()’s
select argument. To get a partial match to
term, you now need to also specify
partial_match = TRUE in the call to
derivatives().
transform_fun() had a copy paste bug in the
definition of the then generic. (#96 @Aariq)
derivatives() with user-supplied
newdata would fail for factor by smooths with
interval = "simultaneous" and would introduce rows with
derivative == 0 with interval = "confidence" because it
didn’t subset the rows of newdata for the specific level of
the by factor when computing derivatives. (#102 @sambweber)
evaluate_smooth() can now handle random effect
smooths defined using an ordered factor. (#99 @StefanoMezzini)
smooth_estimates() can now handle
s(x, z, a),te(), t2(), &
ti()), e.g. te(x, z, a)s(x, f, bs = "fs")s(f, bs = "re")penalty() provides a tidy representation of the
penalty matrices of smooths. The tidy representation is most suitable
for plotting with ggplot().
A draw() method is provided, which represents the
penalty matrix as a heatmap.
newdata argument to smooth_estimates()
has been changed to data as was originally intended.Partial residuals for models can be computed with
partial_residuals(). The partial residuals are the weighted
residuals of the model added to the contribution of each smooth term (as
returned by predict(model, type = "terms").
Wish of #76 (@noamross)
Also, new function add_partial_residuals() can be used
to add the partial residuals to data frames.
Users can now control to some extent what colour or fill scales
are used when plotting smooths in those draw() methods that
use them. This is most useful to change the fill scale when plotting 2D
smooths, or to change the discrete colour scale used when plotting
random factor smooths (bs = "fs").
The user can pass scales via arguments discrete_colour
and continuous_fill.
The effects of certain smooths can be excluded from data
simulated from a model using simulate.gam() and
predicted_samples() by passing exclude or
terms on to predict.gam(). This allows for
excluding random effects, for example, from model predicted values that
are then used to simulate new data from the conditional distribution.
See the example in predicted_samples().
Wish of #74 (@hgoldspiel)
draw.gam() and related functions gain arguments
constant and fun to allow for user-defined
constants and transformations of smooth estimates and confidence
intervals to be applied.
Part of wish of Wish of #79.
confint.gam() now works for 2D smooths
also.
smooth_estimates() is an early version of code to
replace (or more likely supersede) evaluate_smooth().
smooth_estimates() can currently only handle 1D smooths of
the standard types.
The meaning of parm in confint.gam has
changed. This argument now requires a smooth label to match a smooth. A
vector of labels can be provided, but partial matching against a smooth
label only works with a single parm value.
The default behaviour remains unchanged however; if parm
is NULL then all smooths are evaluated and returned with
confidence intervals.
data_class() is no longer exported; it was only ever
intended to be an internal function.
confint.gam() was failing on a tensor product smooth
due to matching issues. Reported by @tamas-ferenci #88
This also fixes #80
The vdiffr package is now used conditionally in package tests. Reported by Brian Ripley #93
draw.gam() with scales = "fixed" now
applies to all terms that can be plotted, including 2d smooths.
Reported by @StefanoMezzini #73
dplyr::combine() was deprecated. Switch to
vctrs::vec_c().
draw.gam() with scales = "fixed" wasn’t
using fixed scales where 2d smooths were in the model.
Reported by @StefanoMezzini #73
draw.gam() can include partial residuals when
drawing univariate smooths. Use residuals = TRUE to add
partial residuals to each univariate smooth that is drawn. This feature
is not available for smooths of more than one variable, by smooths, or
factor-smooth interactions (bs = "fs").
The coverage of credible and confidence intervals drawn by
draw.gam() can be specified via argument
ci_level. The default is arbitrarily 0.95 for
no other reason than (rough) compatibility with
plot.gam().
This change has had the effect of making the intervals slightly narrower than in previous versions of gratia; intervals were drawn at ± 2 × the standard error. The default intervals are now drawn at ± ~1.96 × the standard error.
New function difference_smooths() for computing
differences between factor smooth interactions. Methods available for
gam(), bam(), gamm() and
gamm4::gamm4(). Also has a draw() method,
which can handle differences of 1D and 2D smooths currently (handling 3D
and 4D smooths is planned).
New functions add_fitted() and
add_residuals() to add fitted values (expectations) and
model residuals to an existing data frame. Currently methods available
for objects fitted by gam() and
bam().
data_sim() is a tidy reimplementation of
mgcv::gamSim() with the added ability to use sampling
distributions other than the Gaussian for all models implemented.
Currently Gaussian, Poisson, and Bernoulli sampling distributions are
available.
smooth_samples() can handle continuous by variable
smooths such as in varying coefficient models.
link() and inv_link() now work for all
families available in mgcv, including the location, scale,
shape families, and the more specialised families described in
?mgcv::family.mgcv.
evaluate_smooth(), data_slice(),
family(), link(), inv_link()
methods for models fitted using gamm4() from the
gamm4 package.
data_slice() can generate data for a 1-d slice (a
single variable varying).
The colour of the points, reference lines, and simulation band in
appraise() can now be specified via arguments
point_col,point_alpha,ci_colci_alphaline_colThese are passed on to qq_plot(),
observed_fitted_plot(),
residuals_linpred_plot(), and
residuals_hist_plot(), which also now take the new
arguments were applicable.
Added utility functions is_factor_term() and
term_variables() for working with models.
is_factor_term() identifies is the named term is a factor
using information from the terms() object of the fitted
model. term_variables() returns a character vector of
variable names that are involved in a model term. These are strictly for
working with parametric terms in models.
appraise() now works for models fitted by
glm() and lm(), as do the underlying functions
it calls, especially qq_plot.
appraise() also works for models fitted with family
gaulss(). Further location scale models and models fitted
with extended family functions will be supported in upcoming
releases.
datagen() is now an internal function and
is no longer exported. Use data_slice() instead.
evaluate_parametric_term() is now much stricter and
can only evaluate main effect terms, i.e. those whose order, as stored
in the terms object of the model is
1.
The draw() method for derivatives() was
not getting the x-axis label for factor by smooths correctly, and
instead was using NA for the second and subsequent levels
of the factor.
The datagen() method for class "gam"
couldn’t possibly have worked for anything but the simplest models and
would fail even with simple factor by smooths. These issues have been
fixed, but the behaviour of datagen() has changed, and the
function is now not intended for use by users.
Fixed an issue where in models terms of the form
factor1:factor2 were incorrectly identified as being
numeric parametric terms. #68
New functions link() and inv_link() to
access the link function and its inverse from fitted models and family
functions.
Methods for classes: "glm", "gam",
"bam", "gamm" currently. #58
Adds explicit family() methods for objects of
classes "gam", "bam", and
"gamm".
derivatives() now handles non-numeric when creating
shifted data for finite differences. Fixes a problem with
stringsAsFactors = FALSE default in R-devel. #64
gratia now uses the mvnfast package for random
draws from a multivariate normal distribution
(mvnfast::rmvn()). Contributed by Henrik Singmann
New function basis() for generating tidy
representations of basis expansions from an mgcv-like
definition of a smooth, e.g. s(), te(),
ti(), or t2(). The basic smooth types also
have a simple draw() method for plotting the basis.
basis() is a simple wrapper around
mgcv::smoothCon() with some post processing of the basis
model matrix into a tidy format. #42
New function smooth_samples() to draw samples of
entire smooth functions from their posterior distribution. Also has a
draw() method for plotting the posterior samples.
draw.gam() would produce empty plots between the
panels for the parametric terms if there were 2 or more parametric terms
in a model. Reported by @sklayn #39.
derivatives() now works with factor by smooths,
including ordered factor by smooths. The function also now works
correctly for complex models with multiple covariates/smooths. #47
derivatives() also now handles 'fs'
smooths. Reported by @tomand-uio #57.
evaluate_parametric_term() and hence
draw.gam() would fail on a ziplss() model
because i) gratia didn’t handle parametric terms in models with
multiple linear predictors correctly, and ii) gratia didn’t
convert to the naming convention of mgcv for terms in higher
linear predictors. Reported by @pboesu #45