ggpmisc is based on the grammar of graphics implemented in ggplot2, the idea that you can build every graph from the same components: a data set, a coordinate system, and geoms—visual marks that represent data points. If you are not already familiar with this grammar and ggplot2 you should visit the ggplot2 Cheat Sheet first, and afterwards come back to this Cheat Sheet.
Differently to ggplot2, no geometries with the new
stats as default are provided. The plot layers described here are always
added with a stat, and when necessary, their default
geom argument can be overridden. The default geoms
for the statistics described below are from packages
ggplot2 and ggpp.
Most of the layer functions in ggpmisc aim at making it easier to add to plots information derived from model fitting, tests of significance and some summaries. All layer functions work as expected with groups and facets.
stat_correlation() computes parametric or
non-parametric regression coefficients and optionally their confidence
interval.In general these layer functions have additional features than those in ggplot2 or support different kinds of models. They add one or more curves and confidence band.
stat_poly_line() fits a linear model.
stat_quant_line() and stat_quant_band()
fit a quantile regression model.
stat_ma_line() fits a major axis
regression.
stat_fit_augment() fits any model supported by a
broom::augment() method.
These layer functions have no equivalent in ggplot2.
They flexibly construct labels to add a layer with one or more equations
and/or estimates of parameters a fitted model. In the model is a true
polynomial, the “label” can be formatted to be parsed into an R
expression, or as LaTeX
or markdown
encoded character strings. The numeric values of the estimates are also
returned and can be used to construct a label within a call to
aes().
stat_poly_eq() is similar to
ggplot2::stat_smooth() and fits a linear model.
stat_quant_eq() is similar to
ggplot2::stat_quantile() and fits a quantile regression
model.
stat_ma_eq() fits a major axis regression.
stat_fit_tidy() fits any model supported by a
broom::tidy() method.
stat_fit_tb() fits any model supported by a
broom::tidy() method. Adds an ANOVA or Summary table. Which
columns are included and their naming can be set by the user.stat_multcomp() fits a model, computes ANOVA and
subsequently calls multcomp to test the significance of
Tukey, Dunnet or arbitrary sets of pairwise contrasts, with a choice of
the adjustment method for the P-values. Significance of
differences can be indicated with letters, asterisks or
P-values. Sizes of differences are also computed and available
for user-assembled labels.stat_peaks() finds and labels peaks (= local
maxima).
stat_valleys() finds and labels valleys (= local
minima).
These plots are frequently used with gene expression data, and each
of the many genes labelled based on the ternary outcome from a
statistical test. Data are usually, in addition transformed.
ggpmisc provides several variations on continuous,
colour, fill and shape scales, with defaults set as needed. Scales
support log fold-change (logFC), false discovery ratio
(FDR), P-value (Pvalue) and binary or
ternary test outcomes (outcome).
Most of the functions used to generate formatted labels in layers and scales are also exported.
Learn more at docs.r4photobiology.info/ggpmisc.