gratia 0.8.1.27

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 that 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().

New features

• 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.)

• data_sim() can now simulate response data from gamma, Tweedie and ordered categorical distributions.

• 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.

• 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.dervivatives() 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. Instead, 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 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.

Bug fixes

• 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 not plottable 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

gratia 0.8.1

CRAN release: 2023-02-02

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.

gratia 0.7.2

CRAN release: 2022-03-17

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.

• fitted_values() insures that data (and hence the returned object) is a tibble rather than a common or garden data frame.

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.

gratia 0.7.1

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.

New features

• 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.

Bug fixes

• 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 0.7.0

CRAN release: 2022-02-07

Major changes

• 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.

Deprecated functions

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().

New features

• 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.

Bug fixes

• 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

gratia 0.6.0

CRAN release: 2021-04-18

Major changes

• 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().

New features

• 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.

User visible changes

• 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().

Bug fixes

• 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)

gratia 0.5.1

CRAN release: 2021-01-23

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.

User visible changes

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

gratia 0.5.0

CRAN release: 2021-01-10

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.

Bug Fixes

• confint.gam() was failing on a tensor product smooth due to matching issues. Reported by @tamas-ferenci #88

This also fixes #80

1. which was a related issue with selecting a specific smooth.
• The vdiffr package is now used conditionally in package tests. Reported by Brian Ripley #93

gratia 0.4.1

CRAN release: 2020-05-30

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 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_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 location 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_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.

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 0.3.1

CRAN release: 2020-03-29

New features

• 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

gratia 0.3.0

CRAN release: 2020-01-19

New features

• 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.

Bug fixes

• 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