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.

First CRAN submission of gratia

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.