ggpmisc 0.4.1

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

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