Version 0.4.4 includes enhancements. An issue raised in GitHub and a question in StackOverflow asked for the possibility of changing how fitted lines are plotted based on the “goodness” of the fit. In addition an old question in StackOverflow highlighted the need of more intuitive support for annotations based on
stats::cor.test(). In addition to implementing these enhancements we continued adding support for flipping of statistics controlled through formal parameter
orientation as implemented in ‘ggplot2’ since version 3.3.0.
Changes compared to version 0.4.3, the previous version in CRAN are:
- Add new function
stat_correlation()to annotate plots with correlation estimates, their P-value, a test statistic and n computed with
stats::cor.test(). In addition to formatted character strings, numeric values are included in the returned data frame to facilitate conditional display.
stat_poly_line()to optionally add columns
methodto the returned data frame. Code breaking: This statistic no longer supports fitting of splines with methods such as
loess. This could potentially break user code, in which case the solution is to use
stat_ma_line()to optionally add columns
methodto the returned data frame. (As only a slope can be fitted,
stat_quant_band()to optionally add
methodcolumns to the returned data frame. (No exact equivalent of
r.squaredexists for quantile regression.)
stat_fit_residuals()to optionally return weighted
stat_valleys()to allow flipping with new parameter
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 0.4.4 is on its way to CRAN.
Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/ggpmisc/issues