gratia 0.9.0

Breaking changes

• 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  coord_map() being soft-deprecated ("superseded") for a few minor versions of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  coord_map() is superseded, it didn't receive the updates to the guides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.

Defunct and deprecated functions and arguments

Defunct

• evaluate_smooth() was deprecated in gratia version 0.7.0. This function and
  all it's methods have been removed from the package. Use smooth_estimates()
  instead.

Deprecated functions

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().

Deprecated arguments

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.

User visible changes

• 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}.

New features

• 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 ...

gratia version 0.8.1 on CRAN

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.

gratia 0.8.1

User visible changes

• 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.

New features

• draw.gam() can now handle tensor products that include a marginal random
  effect smooth. Beware plotting such smooths if there are many levels,
  however, as a separate surface plot will be produced for each level.

Bug fixes

• Additional 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 0.8.0

User visible changes

• {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.

New features

• 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

Bug fixes

• 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.

gratia version 0.7.3 is released and on CRAN

gratia 0.7.3

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.

User visible changes

• Plots of smooths now use "Partial effect" for the y-axis label in place of "Effect", to better indicate what is displayed.

New features

• 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.

Bug fixes

• 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 #150

gratia version 0.7.2 is released and on CRAN

gratia 0.7.2 is available and on CRAN

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

New features

• 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.

Bug fixes

• 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).

gratia version 0.7.0 now on CRAN

gratia version 0.7.0 released

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().
• New helper functions typical_values(), factor_combos() and data_combos() for quickly creating data sets for producing predictions from
  fitted models where some of the covariates are fixed at come typical or representative values.
• 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.

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.

gratia version 0.5.1 now on CRAN

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,

User visible changes

• The newdata argument to smooth_estimates() has been changed to data as
  was originally intended.

New features

• smooth_estimates() can now handle

  • bivariate and multivariate thinplate regression spline smooths, e.g.
    s(x, z, a),
  • tensor product smooths (te(), t2(), & ti()), e.g. te(x, z, a)
  • factor smooth interactions, e.g. s(x, f, bs = "fs")
  • random effect smooths, e.g. 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.

gratia version 0.5.0 now on CRAN

gratia 0.5.0

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.

New features

• 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.

User visible changes

• 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.

Version 0.4.1 released to CRAN

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.

gratia 0.4.1

User visible changes

• draw.gam() with scales = "fixed" now applies to all terms that can be
  plotted, including 2d smooths.

  Reported by @StefanoMezzini #73

Bug fixes

• 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

gratia 0.4.0

New features

• 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.

User visible changes

• 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.

Bug fixes

• 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

gratia version 0.3.1

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.

New features

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

Bug fixes

• Updated gratia to work with tibble versions >= 3.0

Bug fix release

This release fixes a bug in the use of the select argument to draw.gam(), which was resulting in the wrong smooths being plotted.