ggpmisc 0.4.3

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Version 0.4.3 contain the bug fix implemented in version 0.4.2-2, which did not make it to CRAN. (See bug listed under 1. in the post ggpmisc 0.4.2.)

New

  1. Add stat_ma_line() and stat_ma_eq() implementing support for major axis (MA), standard major axis (SMA), ranged major axis (RMA) and ordinary least squares (OLS) using function lmodel2() from package ‘lmodel2’.

Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.

NOTE: Version 0.4.3 is on its way to CRAN.

Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/ggpmisc/issues

ggpmisc 0.4.2

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During the major updates to 0.4.0 and 0.4.1 some bugs slipped through various tests. Versions 0.4.2, 0.4.2-1 and 0.4.2-2 contain fixes to these bugs. The bug fixed in 0.4.2-2 triggered an error only when R had been built with specific compilers.

Bugs fixed

  1. Error in stat_poly_eq() and stat_quant_eq() under some Linux builds of R, including when used in RStudio Cloud. This bug did not affect Windows.
  2. Failure to find after_stat() when instead of attaching the package with library(ggpmisc) statistics were called using the ggpmisc::<name> notation.
  3. Remove or convert to suggests some dependencies no longer needed after the split of ‘ggpp’.

Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.

NOTE: Version 0.4.2-2 addressing all three bugs was not submitted to CRAN but instead the fix was released in version 0.4.3 now in CRAN.

Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/ggpmisc/issues

ggpmisc 0.4.1

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Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.

Overview

This update is special in that it was built with the input of excellent ideas and code contributions from users. I learnt a lot myself and these improvements have made ‘ggpmisc’ more useful in general and for myself. Support for quantile regression is now, I hope, close to its final shape. Support for the new ‘ggplot2’ feature: orientation is implemented in the statistics where it is most useful, and can be also be changed, more intuitively, through the model formula. Of the planned enhancements, implementing support for major axis regression, remains in the to do list. Both stat_poly_eq() and stat_quant_eq() now return additional labels, plus some numeric values to facilitate conditional display. Much of the code used to generate the text labels has been improved, and markdown formatting tested.

The suggestion from Mark Neal of adding support for quantile regression partly addressed in ggpmisc 0.4.0 has lead to additional enhancements in this version. The idea of supporting confidence bands for quantile regression came from Samer Mouksassi who also provided code and examples for different types of quantile regression. Additional suggestions from Mark Neal, Carl and other users have lead to bug fixes as well as to an interface with better defaults for arguments (see issue #1).

Changes compared to ‘ggpmisc’ 0.4.0

Enhancements

  • Support robust regression using rlm and the use of function objects as argument to method in stat_poly_eq().
  • Support in stat_poly_eq() and stat_quant_eq() formula = x ~ y and other models in which the explanatory variable is y in addition to models with x as explanatory variable.
  • stat_poly_eq() and stat_quant_eq() now pass to the geom by default a suitable value as argument to parse depending on output.type (enhancement suggested by Mark Neal in issue #11).
  • stat_poly_eq() and stat_quant_eq() return the coefficient estimates as numeric columns in data when output.type = "numeric"  (problem with coefs.ls reported by cgnolte in issue #12).
  • stat_poly_eq() now supports optional use of lower case for r2 and p-value.
  • Revise stat_poly_eq() and stat_quant_eq() so that by default they keep trailing zeros according to the numbers of significant digits given by coef.digits. A new parameter coef.keep.zeros can be set to FALSE to restore the deletion of trailing zeros. Trailing zeros in the equation will be rendered to the plot only if output.type is other than "expression".
  • Add stat_poly_line(),  a new interface to ggplot2::stat_smooth() accepting formula = x ~ y and other models in which the explanatory variable is y rather than x or setting orientation = "y". In contrast to ggplot2::stat_smooth(), stat_poly_line() has always "lm" as default method.
  • Add stat_quant_line() which is an adaptation of ggplot2::stat_smooth() and ggplot2::stat_quantile() accepting formula = x ~ y and other models in which the explanatory variable is y rather than x or setting orientation = "y" to fit models with x as explanatory variable. This change makes it possible to add to a plot a double quantile regression. stat_quant_line() supports plotting of confidence bands for quantile regression using ggplot2::geom_smooth() to create the plot layer.
  • Add stat_quant_band() which plots quantile regressions for three quantiles as a band plus a line, accepting formula = x ~ y and other models in which the explanatory variable is y rather than x or setting orientation = "y" to fit models with x as explanatory variable.
  • Add support for quantile regression rq, robust regression rlm, and resistant regression lqs and function objects to stat_fit_residuals() and stat_fit_deviations().
  • Support use of stat_fit_residuals() and stat_fit_deviations() with formula = x ~ y and other models in which the explanatory variable is y in addition to models with x as explanatory variable.
  • Add weights to returned values by stat_fit_residuals() and stat_fit_deviations() and add support for the weight aesthetic as input for parameter weights of  model fit functions.

Bugs fixed

  • Fix bug in stat_poly_eq() and stat_quant_eq() resulting in mishandling of formulas using the + 0 notation (reported by orgadish in issue #10).
  • Fix bug in stat_poly_eq() and stat_quant_eq() resulting in bad/non-syntactical character strings for eq.label when output.type was different from its default of "expression".

Documentation web site at http://docs.r4photobiology.info/gginnards/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.

NOTE: The new version of the package is on its way to CRAN.

Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/gginnards/issues

ggpp 0.4.2

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Documentation web site at http://docs.r4photobiology.info/ggpp/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.

Overview

The initial implementation and user interface of three apply statistics first introduced in ‘ggpmisc’ 0.3.6 has been revised to expand their usefulness and to make them less error-prone, while the
fourth one is now defunct.

Changes compared to ‘ggpp’ 0.4.1

Enhancements

  • Update stat_apply_group() to support summary functions like quantile() that return vectors with more than one value but shorter than the original number of observations.
  • Update stat_summary_xy() and stat_apply_group() to return NA x and/or y when .fun.x or .fun.y are not passed anargument. This is a code breaking change with respect to the previous (unstable) version.
  • Update stat_summary_xy() and stat_centroid() to support functions that return a one row data frame, like those defined in ‘ggplot2’ to be passed as argument to parameter fun.data of ggplot2::stat_summary(), such as mean_se, mean_cl_boot, etc.

Bugs fixed

  • Fix bug in stat_centroid(), stat_summary_xy() and stat_apply_group() resulting in the return of a long data frame with NA values instead of a data frame with fewer rows.
  • Remove stat_apply_panel() , as it was redundant. (Grouping can be modified per layer when needed.)

Warning

The default argument for geom in stat_centroid() is likely to change in the near future. Otherwise, the three statistics can be considered now stable.

Please raise issues concerning bugs or enhancements to this package through GitHub at https://github.com/aphalo/ggpp/issues. Pull requests are also welcome.

NOTE: The updated ‘ggpp’ (0.4.2) is on its way to CRAN. The latest development version of the package can be installed from GitHub.

remotes::install_github("aphalo/ggpp")