ggplot-based graphics and useful functions for GAMs fitted using the mgcv package
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This patch release is largely motivated to fix a few bugs that came to light recently as I was teaching my GAM course for Physalia and preparing a paper for submission to the Journal of Open Source Software. Version 0.9.1 was never released (submission was rejected by CRAN as the package vignettes took the package over the 5Mb limit and CRAN finally said "Nope").
The entries below summarise the changes in this version of gratia. Nothing major here, but I have started building in support for location, scale, shape families in fitted_samples()
, although currently only the location parameter of those models is supported.
parametric_effects()
slightly escaped the great renaming that happened fortype
and term
did not gain a prefix .
. This is now.type
and .term
.Plots of random effects are now labelled with their smooth label. Previously,
the title was taken fro the variable involved in the smooth, but this doesn't
work for terms like s(subject, continuous_var, bs = "re")
for random slopes,
which previsouly would have the title "subject"
. Now such terms will have
title "s(subject,continuous_var)"
. Simple random intercept terms,
s(subject, bs = "re")
, are now titled "s(subject)"
. #287
The vignettes
custom-plotting.Rmd
, andposterior-simulation.Rmd
vignettes/articles
and thus are no longer available as packagefitted_samples()
now works for gam()
models with multiple linear.parameter
in the returnedpartial_residuals()
was computing partial residuals from the deviance
residuals. For compatibility with mgcv::plot.gam()
, partial residuals are
now computed from the working residuals. Reported by @wStockhausen #273
appraise()
was not passing the ci_col
argument on qq_plot()
and
worm_plot()
. Reported by Sate Ahmed.
Couldn't pass mvn_method
on to posterior sampling functions from user facing
functions fitted_samples()
, posterior_samples()
, smooth_samples()
,
derivative_samples()
, and repsonse_derivatives()
. Reported by @stefgehrig
#279
fitted_values()
works again for quantile GAMs fitted by qgam()
.
confint.gam()
was not applying shift
to the estimate and upper and lower
interval. #280 reported by @TIMAVID & @rbentham
parametric_effects()
and draw.parametric_effects()
would forget about the
levels of factors (intentionally), but this would lead to problems with
ordered factors where the ordering of levels was not preserved. Now,
parametric_effects()
returns a named list of factor levels as attribute
"factor_levels"
containing the required information and the order of levels
is preserved when plotting. #284 Reported by @mhpob
parametric_effects()
would fail if there were parametric terms in the model
but they were all interaction terms (which we don't currently handle). #282
Published by gavinsimpson 7 months ago
Many functions now return objects with different named variables. In order to
avoid clashes with variable names used in user's models or data, a period
(.
) is now being used as a prefix for generated variable names. The
functions whose names have changed are: smooth_estimates()
,
fitted_values()
, fitted_samples()
, posterior_samples()
, derivatives()
,
partial_derivatives()
, and derivative_samples()
. In addition,
add_confint()
also adds newly-named variables.
1. `est` is now `.estimate`,
2. `lower` and `upper` are now `.lower_ci` and `.upper_ci`,
3. `draw` and `row` and now `.draw` and `.row` respectively,
4. `fitted`, `se`, `crit` are now `.fitted`, `.se`, `.crit`, respectively
5. `smooth`, `by`, and `type` in `smooth_estimates()` are now `.smooth`,
`.by`, `.type`, respectively.
derivatives()
and partial_derivatives()
now work more like
smooth_estimates()
; in place of the var
and data
columns, gratia now
stores the data variables at which the derivatives were evaluated as columns
in the object with their actual variable names.
The way spline-on-the-sphere (SOS) smooths (bs = "sos"
) are plotted has
changed to use ggplot2::coord_sf()
instead of the previously-used
ggplot2::coord_map()
. This changed has been made as a result of
coord_map()
being soft-deprecated ("superseded") for a few minor versions of
ggplot2 by now already, and changes to the guides system in version 3.5.0 of
ggplot2.
The axes on plots created with coord_map()
never really worked
correctly and changing the angle of the tick labels never worked. As
coord_map()
is superseded, it didn't receive the updates to the guides
system and a side effect of these changes, the code that plotted SOS smooths
was producing a warning with the release of ggplot2 version 3.5.0.
The projection settings used to draw SOS smooths was previously controlled via
arguments projection
and orientation
. These arguments do not affect
ggplot2::coord_sf()
, Instead the projection used is controlled through new
argument crs
, which takes a PROJ string detailing the projection to use or
an integer that refers to a known coordinate reference system (CRS). The
default projection used is +proj=ortho +lat_0=20 +lon_0=XX
where XX
is the
mean of the longitude coordinates of the data points.
evaluate_smooth()
was deprecated in gratia version 0.7.0. This function andsmooth_estimates()
The following functions were deprecated in version 0.9.0 of gratia. They will
eventually be removed from the package as part of a clean up ahead of an
eventual 1.0.0 release. These functions will become defunct by version 0.11.0 or
1.0.0, whichever is released soonest.
evaluate_parametric_term()
has been deprecated. Use parametric_effects()
instead.
datagen()
has been deprecated. It never really did what it was originally
designed to do, and has been replaced by data_slice()
.
To make functions in the package more consistent, the arguments select
,
term
, and smooth
are all used for the same thing and hence the latter two
have been deprecated in favour of select
. If a deprecated argument is used, a
warning will be issued but the value assigned to the argument will be assigned
to select
and the function will continue.
smooth_samples()
now uses a single call to the RNG to generate draws from
the posterior of smooths. Previous to version 0.9.0, smooth_samples()
would
do a separate call to mvnfast::rmvn()
for each smooth. As a result, the
result of a call to smooth_samples()
on a model with multiple smooths will
now produce different results to those generated previously. To regain the
old behaviour, add rng_per_smooth = TRUE
to the smooth_samples()
call.
Note, however, that using per-smooth RNG calls with method = "mh"
will be
very inefficient as, with that method, posterior draws for all coefficients
in the model are sampled at once. So, only use rng_per_smooth = TRUE
with
method = "gaussian"
.
The output of smooth_estimates()
and its draw()
method have changed for
tensor product smooths that involve one or more 2D marginal smooths. Now,
if no covariate values are supplied via the data
argument,
smooth_estimates()
identifies if one of the marginals is a 2d surface and
allows the covariates involved in that surface to vary fastest, ahead of terms
in other marginals. This change has been made as it provides a better default
when nothing is provided to data
.
This also affects draw.gam()
.
fitted_values()
now has some level of support for location, scale, shape
families. Supported families are mgcv::gaulss()
, mgcv::gammals()
,
mgcv::gumbls()
, mgcv::gevlss()
, mgcv::shash()
, mgcv::twlss()
, and
mgcv::ziplss()
.
gratia now requires dplyr versions >= 1.1.0 and tidyselect >= 1.2.0.
A new vignette Posterior Simulation is available, which describes how to
do posterior simulation from fitted GAMs using {gratia}.
Soap film smooths using basis bs = "so"
are now handled by draw()
,
smooth_estimates()
etc. #8
response_derivatives()
is a new function for computing derivatives of the
response with respect to a (continuous) focal variable. First or second
order derivatives can be computed using forward, backward, or central
finite differences. The uncertainty in the estimated derivative is determined
using posterior sampling via fitted_samples()
, and hence can be derived
from a Gaussian approximation to the posterior or using a Metropolis Hastings
sampler (see below.)
derivative_samples()
is the work horse function behind
response_derivatives()
, which computes and returns posterior draws of the
derivatives of any additive combination of model terms. Requested by
@jonathanmellor #237
data_sim()
can now simulate response data from gamma, Tweedie and ordered
categorical distributions.
data_sim()
gains two new example models "gwf2"
, simulating data only from
Gu & Wabha's f2 function, and "lwf6"
, example function 6 from Luo & Wabha
(1997 JASA 92(437), 107-116).
data_sim()
can also simulate data for use with GAMs fitted using
family = gfam()
for grouped families where different types of data in
the response are handled. #266 and part of #265
fitted_samples()
and smooth_samples()
can now use the Metropolis Hastings
sampler from mgcv::gam.mh()
, instead of a Gaussian approximation, to sample
from the posterior distribution of the model or specific smooths
respectively.
posterior_samples()
is a new function in the family of fitted_samples()
and smooth_samples()
. posterior_samples()
returns draws from the
posterior distribution of the response, combining the uncertainty in the
estimated expected value of the response and the dispersion of the response
distribution. The difference between posterior_samples()
and
predicted_samples()
is that the latter only includes variation due to
drawing samples from the conditional distribution of the response (the
uncertainty in the expected values is ignored), while the former includes
both sources of uncertainty.
fitted_samples()
can new use a matrix of user-supplied posterior draws.
Related to #120
add_fitted_samples()
, add_predicted_samples()
, add_posterior_samples()
,
and add_smooth_samples()
are new utility functions that add the respective
draws from the posterior distribution to an existing data object for the
covariate values in that object: obj |> add_posterior_draws(model)
. #50
basis_size()
is a new function to extract the basis dimension (number of
basis functions) for smooths. Methods are available for objects that inherit
from classes "gam"
, "gamm"
, and "mgcv.smooth"
(for individual smooths).
data_slice()
gains a method for data frames and tibbles.
typical_values()
gains a method for data frames and tibbles.
fitted_values()
now works with models fitted using the mgcv::ocat()
family. The predicted probability for each category is returned, alongside a
Wald interval created using the standard error (SE) of the estimated
probability. The SE and estimated probabilities are transformed to the logit
(linear predictor) scale, a Wald credible interval is formed, which is then
back-transformed to the response (probability) scale.
fitted_values()
now works for GAMMs fitted using mgcv::gamm()
. Fitted
(predicted) values only use the GAM part of the model, and thus exclude the
random effects.
link()
and inv_link()
work for models fitted using the cnorm()
family.
A worm plot can now be drawn in place of the QQ plot with appraise()
via
new argument use_worm = TRUE
. #62
smooths()
now works for models fitted with mgcv::gamm()
.
overview()
now returns the basis dimension for each smooth and gains an
argument stars
which if TRUE
add significance stars to the output plus a
legend is printed in the tibble footer. Part of wish of @noamross #214
New add_constant()
and transform_fun()
methods for smooth_samples()
.
evenly()
gains arguments lower
and upper
to modify the lower and / or
upper bound of the interval over which evenly spaced values will be generated.
add_sizer()
is a new function to add information on whether the derivative
of a smooth is significantly changing (where the credible interval excludes
0). Currently, methods for derivatives()
and smooth_estimates()
objects
are implemented. Part of request of @asanders11 #117
draw.derivatives()
gains arguments add_change
and change_type
to allow
derivatives of smooths to be plotted with indicators where the credible
interval on the derivative excludes 0. Options allow for periods of decrease
or increase to be differentiated via change_type = "sizer"
instead of the
default change_type = "change"
, which emphasises either type of change in
the same way. Part of wish of @asanders11 #117
draw.gam()
can now group factor by smooths for a given factor into a single
panel, rather than plotting the smooths for each level in separate panels.
This is achieved via new argument grouped_by
. Requested by @RPanczak #89
draw.smooth_estimates()
can now also group factor by smooths for a given
factor into a single panel.
The underlying plotting code used by draw_smooth_estimates()
for most
univariate smooths can now add change indicators to the plots of smooths if
those change indicators are added to the object created by
smooth_estimates()
using add_sizer()
. See the example in
?draw.smooth_estimates
.
smooth_estimates()
can, when evaluating a 3D or 4D tensor product smooth,
identify if one or more 2D smooths is a marginal of the tensor product. If
users do not provide covariate values at which to evaluate the smooths,
smooth_estimates()
will focus on the 2D marginal smooth (or the first if
more than one is involved in the tensor product), instead of following the
ordering of the terms in the definition of the tensor product. #191
For example, in te(z, x, y, bs = c(cr, ds), d = c(1, 2))
, the second
marginal smooth is a 2D Duchon spline of covariates x
and y
. Previously,
smooth_estimates()
would have generated n
values each for z
and x
and
n_3d
values for y
, and then evaluated the tensor product at all
combinations of those generated values. This would ignore the structure
implicit in the tensor product, where we are likely to want to know how the
surface estimated by the Duchon spline of x
and y
smoothly varies with
z
. Previously smooth_estimates()
would generate surfaces of z
and x
,
varying by y
. Now, smooth_estimates()
correctly identifies that one of the
marginal smooths of the tensor product is a 2D surface and will focus on that
surface varying with the other terms in the tensor product.
This improved behaviour is needed because in some bam()
models it is not
always possible to do the obvious thing and reorder the smooths when defining
the tensor product to be te(x, y, z, bs = c(ds, cr), d = c(2, 1))
. When
discrete = TRUE
is used with bam()
the terms in the tensor product may
get rearranged during model setup for maximum efficiency (See Details in
?mgcv::bam
).
Additionally, draw.gam()
now also works the same way.
New function null_deviance()
that extracts the null deviance of a fitted
model.
draw()
, smooth_estimates()
, fitted_values()
, data_slice()
, and
smooth_samples()
now all work for models fitted with scam::scam()
.
Where it matters, current support extends only to univariate smooths.
generate_draws()
is a new low-level function for generating posterior draws
from fitted model coefficients. generate_daws()
is an S3 generic function so
is extensible by users. Currently provides a simple interface to a simple
Gaussian approximation sampler (gaussian_draws()
) and the simple Metropolis
Hasting sample (mh_draws()
) available via mgcv::gam.mh()
. #211
smooth_label()
is a new function for extracting the labels 'mgcv' creates for
smooths from the smooth object itself.
penalty()
has a default method that works with s()
, te()
, t2()
, and
ti()
, which create a smooth specification.
transform_fun()
gains argument constant
to allow for the addition of a
constant value to objects (e.g. the estimate and confidence interval). This
enables a single obj |> transform_fun(fun = exp, constant = 5)
instead of
separate calls to add_constant()
and then transform_fun()
. Part of the
discussion of #79
model_constant()
is a new function that simply extracts the first
coefficient from the estimated model.
link()
, inv_link()
, and related family functions for the ocat()
weren't
correctly identifying the family name and as a result would throw an error
even when passed an object of the correct family.
link()
and inv_link()
now work correctly for the betar()
family in a
fitted GAM.
The print()
method for lp_matrix()
now converts the matrix to a data frame
before conversion to a tibble. This makes more sense as it results in more
typical behaviour as the columns of the printed object are doubles.
Constrained factor smooths (bs = "sz"
) where the factor is not the first
variable mentioned in the smooth (i.e. s(x, f, bs = "sz")
for continuous
x
and factor f
) are now plotable with draw()
. #208
parametric_effects()
was unable to handle special parametric terms like
poly(x)
or log(x)
in formulas. Reported by @fhui28 #212
parametric_effects()
now works better for location, scale, shape models.
Reported by @pboesu #45
parametric_effects
now works when there are missing values in one or more
variables used in a fitted GAM. #219
response_derivatives()
was incorrectly using .data
with tidyselect
selectors.
typical_values()
could not handle logical variables in a GAM fit as mgcv
stores these as numerics in the var.summary
. This affected evenly()
and
data_slice()
. #222
parametric_effects()
would fail when two or more ordered factors were in
the model. Reported by @dsmi31 #221
Continuous by smooths were being evaluated with the median value of the by
variable instead of a value of 1. #224
fitted_samples()
(and hence posterior_samples()
) now handles models with
offset terms in the formula. Offset terms supplied via the offset
argument
are ignored by mgcv:::predict.gam()
and hence are ignored also by gratia
.
Reported by @jonathonmellor #231 #233
smooth_estimates()
would fail on a "fs"
smooth when a multivariate base
smoother was used and the factor was not the last variable specified in the
definition of the smooth: s(x1, x2, f, bs = "fs", xt = list(bs = "ds"))
would work, but s(f, x1, x2, bs = "fs", xt = list(bs = "ds"))
(or any
ordering of variables that places the factor not last) would emit an obscure
error. The ordering of the terms involved in the smooth now doesn't matter.
Reported by @chrisaak #249.
draw.gam()
would fail when plotting a multivariate base smoother used in an
"sz"
smooth. Now, this use case is identified and a message printed
indicating that (currently) gratia doesn't know how to plot such a smooth.
Reported by @chrisaak #249.
draw.gam()
would fail when plotting a multivariate base smoother used in an
"fs"
smooth. Now, this use case is identified and a message printed
indicating that (currently) gratia doesn't know how to plot such a smooth.
Reported by @chrisaak #249.
derivative_samples()
would fail with order = 2
and was only computing
forward finite differences, regardless of type
for order = 1
. Partly
reported by @samlipworth #251.
The draw()
method for penalty()
was normalizing the penalty to the range
0--1, not the claimed and documented -1--1 with argument normalize = TRUE
.
This is now fixed.
smooth_samples()
was failing when data
was supplied that contained more
variables than were used in the smooth that was being sampled. Hence this
generally fail unless a single smooth was being sampled from or the model
contained only a single smooth. The function never intended to retain all the
variables in data
but was written in such a way that it would fail when
relocating the data columns to the end of the posterior sampling object. #255
draw.gam()
and draw.smooth_estimates()
would fail when plotting a
univariate tensor product smooth (e.g. te(x)
, ti(x)
, or t2()
). Reported
by @wStockhausen #260
plot.smooth()
was not printing the factor level in subtitles for ordered
factor by smooths.
Published by gavinsimpson over 1 year ago
Version 0.8.1 of gratia is on CRAN. Version 0.8.0 was not released do to changes necessitated for the 1.1.0 release of dplyr. The full list of changes in the 0.8. and 0.8.1 versions is given below.
smooth_samples()
now returns objects with variables involved in smooths
that have their correct name. Previously variables were named .x1
, .x2
,
etc. Fixing #126 and improving compatibility with compare_smooths()
and
smooth_estimates()
allowed the variables to be named correctly.
gratia now depends on version 1.8-41 or later of the mgcv package.
draw.gam()
can now handle tensor products that include a marginal randomAdditional fixes for changes in dplyr 1.1.0.
smooth_samples()
now works when sampling from posteriors of multiple smooths
with different dimension. #126 reported by @Aariq
{gratia} now depends on R version 4.1 or later.
A new vignette "Data slices" is supplied with {gratia}.
Functions in {gratia} have harmonised to use an argument named data
instead
of newdata
for passing new data at which to evaluate features of smooths. A
message will be printed if newdata
is used from now on. Existing code does
not need to be changed as data
takes its value from newdata
.
Note that due to the way ...
is handled in R, if your R script uses the
data
argument, and is run with versions of gratia prior to 8.0 (when
released; 0.7.3.8 if using the development version) the user-supplied data
will be silently ignored. As such, scripts using data
should check that the
installed version of gratia is >= 0.8 and package developers should update
to depend on versions >= 0.8 by using gratia (>= 0.8)
in DESCRIPTION
.
The order of the plots of smooths has changed in draw.gam()
so that they
again match the order in which smooths were specified in the model formula.
See Bug Fixes below for more detail or #154.
Added basic support for GAMLSS (distributional GAMs) fitted with the
gamlss()
function from package GJRM. Support is currently restricted to a
draw()
method.
difference_smooths()
can now include the group means in the difference,
which many users expected. To include the group means use group_means = TRUE
in the function call, e.g.
difference_smooths(model, smooth = "s(x)", group_means = TRUE
). Note: this
function still differs from plot_diff()
in package itsadug, which
essentially computes differences of model predictions. The main practical
difference is that other effects beyond the factor by smooth, including random
effects, may be included with plot_diff()
.
This implements the main wish of #108 (@dinga92) and #143 (@mbolyanatz)
despite my protestations that this was complicated in some cases (it isn't;
the complexity just cancels out.)
data_slice()
has been totally revised. Now, the user provides the values for
the variables they want in the slice and any variables in the model that are
not specified will be held at typical values (i.e. the value of the
observation that is closest to the median for numeric variables, or the modal
factor level.)
Data slices are now produced by passing name
= value
pairs for the
variables and their values that you want to appear in the slice. For example
m <- gam(y ~ s(x1) + x2 + fac)
data_slice(model, x1 = evenly(x1, n = 100), x2 = mean(x2))
The value
in the pair can be an expression that will be looked up
(evaluated) in the data
argument or the model frame of the fitted model
(the default). In the above example, the resulting slice will be a data frame
of 100 observations, comprising x1
, which is a vector of 100 values spread
evenly over the range of x1
, a constant value of the mean of x2
for the
x2
variable, and a constant factor level, the model class of fac
, for the
fac
variable of the model.
partial_derivatives()
is a new function for computing partial derivatives
of multivariate smooths (e.g. s(x,z)
, te(x,z)
) with respect to one of
the margins of the smooth. Multivariate smooths of any dimension are handled,
but only one of the dimensions is allowed to vary. Partial derivatives are
estimated using the method of finite differences, with forward, backward,
and central finite differences available. Requested by @noamross #101
overview()
provides a simple overview of model terms for fitted GAMs.
The new bs = "sz"
basis that was released with mgcv version 1.18-41 is
now supported in smooth_estimates()
, draw.gam()
, and
draw.smooth_estimates()
and this basis has its own unique plotting method.
#202
basis()
now has a method for fitted GAM(M)s which can extract the estimated
basis from the model and plot it, using the estimated coefficients for the
smooth to weight the basis. #137
There is also a new draw.basis()
method for plotting the results of a call
to basis()
. This method can now also handle bivariate bases.
tidy_basis()
is a lower level function that does the heavy lifting in
basis()
, and is now exported. tidy_basis()
returns a tidy representation
of a basis supplied as an object inheriting from class "mgcv.smooth"
. These
objects are returned in the $smooth
component of a fitted GAM(M) model.
lp_matrix()
is a new utility function to quickly return the linear predictor
matrix for an estimated model. It is a wrapper to
predict(..., type = "lpmatrix")
evenly()
is a synonym for seq_min_max()
and is preferred going forward.
Gains argument by
to produce sequences over a covariate that increment in
units of by
.
ref_level()
and level()
are new utility functions for extracting the
reference or a specific level of a factor respectively. These will be most
useful when specifying covariate values to condition on in a data slice.
model_vars()
is a new, public facing way of returning a vector of variables
that are used in a model.
difference_smooths()
will now use the user-supplied data as points at
which to evaluate a pair of smooths. Also note that the argument newdata
has
been renamed data
. #175
The draw()
method for difference_smooths()
now uses better labels for
plot titles to avoid long labels with even modest factor levels.
derivatives()
now works for factor-smooth interaction ("fs"
) smooths.
draw()
methods now allow the angle of tick labels on the x axis of plots to
be rotated using argument angle
. Requested by @tamas-ferenci #87
draw.gam()
and related functions (draw.parametric_effects()
,
draw.smooth_estimates()
) now add the basis to the plot using a caption.
#155
smooth_coefs()
is a new utility function for extracting the coefficients
for a particular smooth from a fitted model. smooth_coef_indices()
is an
associated function that returns the indices (positions) in the vector of
model coefficients (returned by coef(gam_model)
) of those coefficients that
pertain to the stated smooth.
draw.gam()
now better handles patchworks of plots where one or more of
those plots has fixed aspect ratios. #190
draw.posterior_smooths
now plots posterior samples with a fixed aspect ratio
if the smooth is isotropic. #148
derivatives()
now ignores random effect smooths (for which derivatives
don't make sense anyway). #168
confint.gam(...., method = "simultaneous")
now works with factor by smooths
where parm
is passed the full name of a specific smooth s(x)faclevel
.
The order of plots produced by gratia::draw.gam()
again matches the order
in which the smooths entered the model formula. Recent changes to the
internals of gratia::draw.gam()
when the switch to smooth_estimates()
was
undertaken lead to a change in behaviour resulting from the use of
dplyr::group_split()
, and it's coercion internally of a character vector to
a factor. This factor is now created explicitly, and the levels set to the
correct order. #154
Setting the dist
argument to set response or smooth values to NA
if they
lay too far from the support of the data in multivariate smooths, this would
lead an incorrect scale for the response guide. This is now fixed. #193
Argument fun
to draw.gam()
was not being applied to any parametric terms.
Reported by @grasshoppermouse #195
draw.gam()
was adding the uncertainty for all linear predictors to smooths
when overall_uncertainty = TRUE
was used. Now draw.gam()
only includes the
uncertainty for those linear predictors in which a smooth takes part. #158
partial_derivatives()
works when provided with a single data point at
which to evaluate the derivative. #199
transform_fun.smooth_estimates()
was addressing the wrong variable names
when trying to transform the confidence interval. #201
data_slice()
doesn't fail with an error when used with a model that contains
an offset term. #198
confint.gam()
no longer uses evaluate_smooth()
, which is soft deprecated.
#167
qq_plot()
and worm_plot()
could compute the wrong deviance residuals used
to generate the theoretical quantiles for some of the more exotic families
(distributions) available in mgcv. This also affected appraise()
but only
for the QQ plot; the residuals shown in the other plots and the deviance
residuals shown on the y-axis of the QQ plot were correct. Only the
generation of the reference intervals/quantiles was affected.
Published by gavinsimpson over 2 years ago
This is a minor release for gratia, mainly motivated by a request to fix outputs from examples on M1 Macs where the results printed deviated markedly from the reference output generated on my Linux machine. The full entry for the release in NEWS.md
is reproduced below.
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 AndrewPublished by gavinsimpson over 2 years ago
Following the release of version 0.7.0, a couple of annoying bugs were identified which necessitated a patch release. I had implemented methods to plot partial effects for 3d and 4d smooths so decided to include these early enhancements in the patch release to try to shake out any bugs or problems with the implementation prior to a more substantial point (0.8.0) release later in the year (planned for September 2022 at the latest as gratia is needed for a GAM course). Similarly, the problem that delayed 0.7.1 (below) meant that a new plotting method to handle splines on the sphere snuck in to the release, for the same reasons as handling >2d smooths.
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.
While binaries for Windows and MacOS X systems are being built, you can install version 0.7.2 from R Universe: https://gavinsimpson.r-universe.dev/ui#builds
draw.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.
draw.gam()
and draw.smooth_estimates()
: {gratia} can now handle smooths of 3 or 4 covariates when plotting. As an example of what is possible, the figure below shows the estimated smooths from y ~ s(x,z) + s(year, bs = "cr") + ti(x,z, year, d = c(2,1), bs = c("tp", "cr"))
for a space-time GAM modelling shrimp abundance. The layout has been tweaked a little (via the design
argument to patchwork::plot_layout()
) from the default you get with draw.gam()
but otherwise it is unchanged.
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()
[sic] 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.
fitted_values()
insures that data
(and hence the returned object) is a tibble rather than a common or garden data frame.
draw.gam()
gains argument resid_col = "steelblue3"
that allows the colour of the partial residuals (if plotted) to be changed.
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.
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)
.
Published by gavinsimpson over 2 years ago
I am pleased to announce the release of version 0.7.0 of the gratia package. gratia is intended to make working with generalized additive models (GAMs) easier and to facilitate the production of high quality visualizations of estimated smooths and entire models using the ggplot2 package.
Version 0.7.0 of the package represents a significant milestone: the main user-facing and internal functions for evaluating estimated smooths at covariate values have been entirely replaced by new functions written from the ground up to be easier to extend and maintain than the original functions. These new functions are smooth_estimates()
and parametric_effects()
. Consequently, functions evaluate_smooth()
and evaluate_parametric_term()
are now soft-deprecated; a warning will be issued upon their first usage to encourage the use of the new functions.
smooth_estimates()
and parametric_effects()
are more capable and easier to extend than their deprecated forebears. They can return results for multiple smooth or parametric terms in a single call, while the internals allow for new smooth types that require specialist handling to be added without rewriting the main code base or extensive redesigns.
The main user-facing plotting function draw()
for fitted GAMs and related models has been rewritten to use smooth_estimates()
and parametric_effects()
. Some small differences in behaviour may be encountered, but it is expected that previous code using gratia is backward compatible.
In addition to the major changes described above, version 0.7.0 also introduces a ranges of new functions to make the GAM-related aspects of your life a little bit easier.
fitted_values()
produces fitted or estimated values from the model. These can be on the scale of the link function or the response and a credible interval is provided for the requested coverage on the chosen scale.rootogram()
provides rootogram diagnostics, mainly for count-based models (fitted with families poisson()
, negbin()
, nb()
, and gaussian()
), but other families may be supported in the future. The draw()
method can plot various kinds of rootogram from the results of rootogram()
.typical_values()
, factor_combos()
and data_combos()
for quickly creating data sets for producing predictions fromedf()
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.Additional new features and information of bugs fixed can be found in the news.
The package has a new pkgdown website, with search facility: https://gavinsimpson.github.io/gratia/
Finally, I know the documentation available for the package and individual functions isn't anywhere near as good as it could be. I have tried to provide examples for the user-facing functions in the package. In addition, this version of gratia comes with a Getting Started vignette, which shows some of the main functions for working with GAMs with gratia. Development on the package towards version 0.8.0 will have a focus on providing better documentation and additional vignettes to illustrate the range of functionality in the package.
Published by gavinsimpson over 3 years ago
This release was prompted by an issue with an argument naming choice in the new smooth_estimates()
function. Some additional functionality was completed prior to realising I needed to release 0.5.1,
newdata
argument to smooth_estimates()
has been changed to data
assmooth_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.
Published by gavinsimpson almost 4 years ago
Covid-19- and teaching left me little development time, but a prompt from CRAN to address the use of {vdiffr} 📦 in package tests spurred me to wrap up some of the new features I had committed to the development version.
I also took the opportunity to complete the initial steps on a replacement for (or more accurately a successor to) evaluate_smooth()
. Some early decisions I made when developing evaluate_smooth()
meant that it was increasingly difficult to maintain and add support for more complex models, due to the way I had handled factor by
variable smooths.
The replacement/successor is smooth_estimates()
. At the moment it only handles simple 1-D smooths, but it should be much easier to accommodate other smooth types and more complex models with multiple linear predictors.
Eventually, once smooth_estimates()
can handle the range of smooths and models that evaluate_smooth()
can currently, I'll swap out instances of evaluate_smooth()
from the higher-level functions that rely upon it. At the moment I don't plan on removing evaluate_smooth()
from {gratia}, but its use will be at the very least soft-deprecated.
Some of the News for the release is copied below.
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.
Published by gavinsimpson over 4 years ago
Version 0.4.1 of gratia has been released to CRAN. Version 0.4.0 existed for a short while but the release to CRAN was pulled because of a last minute change needed to accommodate v 1.0.0 of dplyr that had gone overlooked in the testing for 0.4.0.
This gave me an opportunity to fix an additional bug (#73) as well.
The full list of changes is reproduced below for version 0.4.1 and 0.4.0.
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 now 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 ocnfidence 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 chance 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_smooth()
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_col
ci_alpha
line_col
These 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
locational 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_terms()
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
Published by gavinsimpson over 4 years ago
This version of gratia was prompted by changes in the upcoming 4.0.0 release of R, which makes changes to the stringsAsFactors
default to be FALSE
. A number of tests relied inadvertently on the implicit coercion of character vectors to factors and the derivative code made some assumptions about data only contains numeric of factor variables.
In addition, this version of gratia includes new functions for extracting the link functions from models, and has been updated to work with the forthcoming release of the tibble package.
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
Published by gavinsimpson over 5 years ago
This release fixes a bug in the use of the select
argument to draw.gam()
, which was resulting in the wrong smooths being plotted.
Published by gavinsimpson almost 6 years ago
gratia recently reached version 0.2-0 and after some last-minute teething issues related to a new release of the tibble package, gratia was submitted to CRAN.
The package is ready for public release and has been widely tested against a range of estimated models. In particular, the package is now used to support a paper that I've been involved with writing on hierarchical GAMs.