mf has been
used in this package, instead of fm, to signify fitted
model. This has changed in this version as formal parameter
mf.values has been renamed fm.values and
variable mf_tb in values returned by statistics renamed
fm.value. Although these are code breaking
changes, they are likely to cause difficulties only in isolated
cases as defaults rarely need to be overridden.use_label() that greatly simplifies
assembling and mapping combined labels from the values returned by
stat_poly_eq(), stat_ma_eq(),
stat_quant_eq() and stat_correlation().fm.tb.type, fm.class,
fm.method, and fm.formula.chr to the data
returned by stat_fit_tb(), and rename mf_tb
into fm.tb for naming consistency.fm.class, fm.method, and
fm.formula.chr to the data returned by all other statistics
based on model fitting.stat_correlation(). In
the case of method = "pearson" assuming Normal distribution
or estimated by bootstrap. For method = "kendall" and
method = "spearman" only bootstrap estimates. These are
implemented using package ‘confintr’.stat_poly_eq() (implemented using package
‘confintr’).stat_ma_eq().method.label to the data returned by
stat_correlation(), stat_poly_eq(),
stat_ma_eq() and stat_quant_eq().keep_tidy(),
keep_glance() and keep_augment() as wrappers
on methods tidy(), glance() and
augment() from package ‘broom’. These new functions make it
possible to keep a trace of the origin of the “broom-tidied”
outputs.weight aesthetic in
stat_poly_eq(), stat_poly_line(),
stat_quant_eq() and stat_quant_line().stat_poly_eq() and
stat_quant_eq() now retrieved from the returned fitted
model object before constructing the equation label. This makes it
possible model selection within the function passed as argument to
method. (Inspired by an answer read in Stackoverflow.)method.method is now parsed so that it can contain both the name
of a model fit function and the argument to be passed to this function’s
own method parameter. (Backward compatibility is
maintained.)method in the returned data containing a
character string with the method used in the model fit.This update fixes a significant bug. Although the problem, when triggered, is obvious by looking at the plot, please, update.
stat_peaks() and
stat_valleys(). They could return wrong values for peaks
and valleys if the rows in data in the ggplot object were
not sorted by the value of x for all arguments to
span different from null.This is a minor update for compatibility with ‘ggpp’ (>= 0.4.3) and fixing a wrong version number for ‘gginnards’ in DESCRIPTION.
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(). We implemented these requested
enhancements and continued adding support for flipping of statistics
through parameter orientation as implemented in ‘ggplot2’
since version 3.3.0.
Update stat_poly_line() to optionally add columns
n, p.value, r.squared ,
adj.r.squared and method to the returned data
frame. 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_smooth().
Update stat_ma_line() to optionally add columns
n, p.value, r.squared and
method to the returned data frame. (As only a slope can be
fitted, adj.r.squared is irrelevant.)
Update stat_quant_line() and
stat_quant_band() to optionally add n and
method columns to the returned data frame. (No exact
equivalent of r.squared exists for quantile
regression.)
Update stat_fit_residuals() to optionally return
weighted residuals.
Update stat_peaks() and stat_valleys()
to allow flipping with new parameter orientation.
New function stat_correlation() to annotate plots
with correlation estimates, their P-value, a test statistic and n
computed with stats::cor.test(). Numeric values are
included in the returned data frame to facilitate conditional
display.
Add statistics stat_ma_line() and
stat_ma_eq() implementing model II regression based on
package ‘lmodel2’ (major axis, standard major axis, and ranged major
axis regression). Methods coef(), confint()
and predict() for fit objects returned by
lmodel2::lmodel2() are also implemented and exported.
Removed setting of fill to light blue in
stat_quant_band() as there is no safe way of overriding the
geom’s default.
Fix major bug in stat_poly_eq() and
stat_quant_eq() affecting only some R builds, reported and
reproduced for Linux. (Reported by Flavio Lozano-Isla, T. BruceLee and
Lewis Hooper, debugged with the help of Mark B. Neal.) Reported to
affect versions 0.4.0, 0.4.1, 0.4.2 and 0.4.2-1.
Fix a bug remaining in 0.4.2, that could result in
after_stat() not being found. (Reported by Prof. Brian
Ripley and Michael Steinbaugh.)
Changes to Depends, Imports and Suggests, to solve errors and/or to avoid dependencies that are not needed. As a consequence package ‘broom’ is no longer automatically installed as a dependency of ‘ggpmisc’ and if used, will need to be explicitly installed by the user. Several examples are now run only if the necessary packages have been installed (Prof. Brian Ripley, Uwe Ligges and members of the CRAN’s team are thanked for package quality control).
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 examples. 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). Some other enhancements are based on my own needs or ideas.
rlm and for fit
function objects in stat_poly_eq().stat_poly_eq() and
stat_quant_eq() with formula = x ~ y and other
models in which the explanatory variable is y in addition
to models with x as explanatory variable (this was already
supported but the defaults for eq.with.lhs and
eq.x.rhs were hard coded needing manual override while they
are now set dynamically depending on the formula).stat_poly_eq() and stat_quant_eq()
so that they 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) and so that the default
output.type is "markdown" if the argument
passed to geom is one of "richtext" or
"textbox", improving compatibility with package
‘ggtext’.stat_poly_eq() and stat_quant_eq()
so that when output.type = "numeric" they return the
coefficient estimates as numeric columns in
data (problem with coefs.ls column in data
when using facets reported by cgnolte in issue #12).stat_poly_eq() adding support for optional use
of lower case r and p for \(R^2\) and \(P\)-value, respectively.stat_poly_eq() and
stat_quant_eq() resulting in mishandling of formulas using
the + 0 notation to exclude the intercept (reported by
orgadish in issue #10).stat_poly_line(), which is 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
"lm" as default for method irrespective of the
number of observations.stat_quant_line() which is a merge 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 statistic 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.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. By default the band uses "steelblue"
as fill, to distinguish them from confidence bands.rq, robust
regression rlm, and resistant regression lqs
and function objects to stat_fit_residuals()
and stat_fit_deviations() .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.weights to returned values by
stat_fit_residuals() and stat_fit_deviations()
and add support for the weight aesthetic as their input for
parameter weights of the model fit functions.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. Be aware that even if the character
label for the equation contains trailing zeros, if it is parsed into R
an expression (as it is by default) the trailing zeros will be dropped
at this later stage. Trailing zeros in the equation will be rendered
to the plot only if output.type is other than
"expression". Equations and other labels may render
slightly differently than in previous versions as now
sprintf() is used to format all labels.stat_poly_eq() and
stat_quant_eq() that resulted in bad/non-syntactical
character strings for eq.label when
output.type was different from its default of
"expression".Package ‘ggpmisc’ has been split into two packages: ‘ggpp’ containing extensions to the grammar of graphics and ‘ggpmisc’ containing extensions related to plot decorations based on model fits, statistical summaries and other descriptors of the data being plotted. Package ‘ggpmisc’ depends on ‘ggpp’ with no visible changes for users. Package ‘ggpp’ can be loaded instead of ‘ggpmisc’ when only the extensions it contains are needed. Package ‘gginnards’ containing tools for editing ggplot objects as well as tools for inspecting them is an earlier spin-off from ‘gpmisc’.
The changes in this version stem for users’ questions and suggestions. Many thanks!
Add stat_quant_eq() based on quantile regression as
implemented in package ‘quantreg’. (enhancement suggested by Mark
Neal)
Add n.label and n to the values
returned by stat_poly_eq()and stat_quant_eq().
(enhancement suggested by a question from ganidat)
Add r.squared, adj.r.squared,
p.value and n as numeric values
returned in addition to the corresponding character labels
when stat_poly_eq() is called with output.type
other than numeric. Similarly for n and
rho in the case of stat_quant_eq().
(enhancement suggested by a question from Tiptop)
Fix bug in stat_poly_eq() leading to empty returned
value when data contains too few observations to fit the model.
(reported by ganidat)
Add support for quantile regression rq, robust
regression rlm, and resistant regression lqs
and function objects to
stat_fit_deviations().
geom_plot().stat_poly_eq().stat_poly_eq() that resulted in no labels
being displayed for any group when one group has too few distinct
x-values to fit the polynomial (reported by user 5432156
“ganidat” in StackOverflow).geom_linked_text(). Except for the drawing of segments or
arrows this new geometry behaves as
ggplot2::geom_text() . Note: Segments and arrows
are drawn only if the position function used returns both the
repositioned and original coordinates.position_nudge_centre() and
position_nudge_line() compute the direction of nudging and
return both the nudged and original positions.position_nudge_to()
nudges to new user-supplied position(s);
position_nudge_keep() nudges to position(s) based on
user-supplied position shift. These functions return both nudged and
original position(s), which makes possible to draw connecting segments
from text labels to the original position.stat_fit_glance() , stat_fit_augment() ,
stat_fit_tidy() and stat_fit_tb() now import
the tidiers from package ‘generics’ instead of from ‘broom’. As
a result, users must now explicitly load the package where the methods
to be used are defined, such as ‘broom’ or ‘broom.mixed’ or define them
before calling these statistics.glance.args to
stat_fit_glance() , parameter tidy.ars to
stat_fit_tidy() and stat_fit_tb() and
parameter augment.args to stat_fit_augment()
as some specializations of broom::glance(),
broom::tidy() and stat_fit_augment() accept
arguments specific to a given fitting method.stat_fit_tidy() would fail with
quantreg::rq() and any other fit methods that do not return
by default standard error estimates for parameter estimates (Thanks to
Mark Neal for reporting the problem).stat_fit_glance(),
stat_fit_augment() and stat_fit_tidy() to
ensure compatibility with cor.test() and other functions
that require an object rather than a quoted expression as argument for
data .p.digits to
stat_fit_tb().try_tibble.ts() and
try_data_frame() did not handle correctly the conversion of
dates for some time series, which also could affect
ggplot.ts().stat_peaks() and stat_valleys()
generated wrong labels if a Date object was mapped to x
(the bug did not affect POSIX or datetime, and was obvious as it
resulted in a shift in dates by several decades).stat_fit_tb() to support renaming of
terms/parameter names in the table (Suggested by Big Old Dave and Z.
Lin). In addition implement selection, reordering and renaming of
columns and terms/parameters using positional indexes and pattern
matching of truncated names in addition to whole names. Improve
formatting of small P-values.stat_fmt_tb() to support the same expanded
syntax as stat_fit_tb().stat_dens1d_filter(),
stat_dens1d_filter_g() and
stat_dens1d_labels(), to complement existing
stat_dens2d_filter(), stat_dens2d_filter_g()
and stat_dens2d_labels().stat_dens2d_filter(),
stat_dens2d_filter_g() and
stat_dens2d_labels() adding formal parameters
keep.sparse and invert.selection, as available
in the new 1D versions.stat_dens2d_labels() to accept not only
character strings but also functions as argument to
label.fill as the new stat_dens1d_labels()
does.ggplot2::annotate() adding support for
aesthetics npcx and npcy.stat_summary_xy() and
stat_centroid().stat_poly_eq() to support labelling of equations
according to group.output.type "markdown" in
stat_poly_eq() usable with geom_richtext()
from package ‘ggtext’.geom_table_npc().stat_poly_eq().stat_poly_eq().This version implements some new features and fixes bugs in the features introduced in version 0.3.1, please do rise an issue if you notice any remaining bugs! Some reported weaknesses in the documentation have been addressed. This updated version depends on ‘ggplot2’ (>= 3.2.1).
Add support for volcano and quadrant plots of outcomes.
Add geometries geom_vhlines() and
geom_quadrant_lines().
Add convenience scales scale_x_logFC() and
scale_y_logFC() for data expressed as fold change.
Add convenience scales scale_x_Pvalue(),
scale_y_Pvalue(), scale_x_FDR(),
scale_y_FDR().
Add convenience scales scale_colour_outcome(),
scale_fill_outcome() and scale_shape_outcome()
for data expressed as ternary or binary outcomes.
Add conversion functions outcome2factor() and
threshold2factor() to convert vectors of numeric outcomes
into factors with 2 or 3 levels.
Add conversion function xy_outcomes2factor() and
xy_thresholds2factor() to combine two vectors of numeric
outcomes into a 4-level factor.
Improve support for model-fit annotations.
Update stat_poly_eq() so that optionally instead of
text labels it can return numeric values extracted from the fit
object.
Document with examples how to pass weights and covariates to
statistics based on methods from package ‘broom’. Highlight the
differences among stat_poly_eq() and the
stat_fit_xxx() statistics implemented using package
‘broom’.
Revise stat_apply_fun() to allow simultaneous
application of functions to x and y aesthetics, and
handling of diff() and other functions returning slightly
shorter vectors than their input.
Support in stat_fit_tb(),
stat_fit_augment(), stat_fit_tidy() and
stat_fit_glance() the use of character strings as position
arguments for parameters label.x and label.y
when using geoms based on x and y aesthetics in
addition to when using those taking the npcx and
npcy aesthetics.
This is a major update, with a few cases in which old code may need to be revised to work, and many cases in which there will be subtle differences in the positions of labels used as annotations. The many new features may still have some bugs, please do rise an issue if you notice one!
Version requiring ‘ggplot2’ (>= 3.1.0).
Add new geometries, several of them accepting x and
y in npc units through the new aesthetics
npcx and npcy, allowing positioning relative
to plotting area irrespective of native data units and scale limits.
These geometries are useful on their own for annotations in particular
they allow consistent positioning of textual summaries. By default they
do not inherit the plot’s aesthetic mappings making their behaviour
remain by default in-between that of true geometries and that of
annotate().
geom_text_npc() and geom_label_npc()
using aesthetics npcx and npcy.geom_table_npc() using aesthetics npcx and
npcy.geom_plot() and geom_plot_npc() which
can be used to add inset plots to a ggplot.geom_grob() and geom_grob_npc() which
can be used to add inset grobs to a ggplot.geom_x_margin_point(),
geom_y_margin_point(), geom_x_margin_arrow()
and geom_y_margin_arrow() which behave similarly to
geom_hline() and geom_vline() but plot points
or arrows instead of lines. Add geom_x_margin_grob() and
geom_y_margin_grob() with similar behaviour but for adding
grobs.geom_table() and
depended on the old default of inherit.aes=TRUE.stat_apply_panel() and
stat_apply_group().stat_fit_glance() and improve
diagnosis of unsupported input. Replace bad example in the corresponding
documentation (workaround for bug reported by Robert White).Version requiring ‘ggplot2’ (>= 3.0.0), now in CRAN. Low level manipulation and debug methods and functions moved to new package ‘gginnards’ available through CRAN.
stat_poly_eq()
(fixing bug reported by S.Al-Khalidi).stat_fit_tb().stat_fmt_tb() for formatting of
tibbles for addition to plots as tables.stat_quadrat_count() into
stat_quadrant_count() (miss-spelling).Non-CRAN version with additional functionality, but requiring the development version of ‘ggplot2’.
Non-CRAN version with additional functionality, but requiring the development version of ‘ggplot2’ >= 2.2.1.9000 (>= commit of 2017-02-09) from Github. Visit
geom_table(), a geom for adding a layer containing one
or more tables to a plot panel.stat_fit_tb() a stat that computes a tidy tabular
version of the summary or ANOVA table from a model fit.CRAN version
Add stat_quadrat_count() a stat that computes the
number of observations in each quadrant of a plot panel ignoring
grouping.
Fix bugs, one of which is code breaking: the names of returned
parameter estimates have changed in stat_fit_tidy() now
pasting "_estimate" to avoid name clashes with mapped
variables.
stat_fit_tidy() so that it returns
p-values for parameters, in addition to estimates and their
standard errors.geom_debug() adding missing default
arguments.delete_layers(), append_layers(),
move_layers(), shift_layers(),
which_layers(), extract_layers(),
num_layers(), top_layer() and
bottom_layer().Add stat_fit_tidy() implemented using
broom::tidy(). Makes it possible to add the fitted equation
for any fitted model supported by package ‘broom’, as long as the user
supplies within aes() the code to build a label string. Update user
guide.
Fix bug in stat_poly_equation() eq.x.rhs
argument ignored when using expressions.
try_tibble() and
try_data_frame() which made them fail silently with some
objects of class "ts" in the case of numeric (decimal date)
index for time. In addition lack of special handling for classes
"yearmon" and "yearqrt" from package ‘zoo’,
lead to erroneous date shifts by a few days.ggplot.ts() and
ggplot.xts().label.fill in
stat_dens2d_labels() from NA to
"".stat_dens2d_labels()
useful.Add stat_dens2d_labels(), a statistic that resets
label values to NA by default, or any character string
supplied as argument, in regions of a panel with high density of
observations.
Add stat_den2d_filter(), a statistic that
filters-out/filters-in observations in regions of a panel with high
density of observations. These two statistics are useful for labeling or
highlighting observations in regions of a panel with low density. Both
stats use a compute_panel function.
Add stat_den2d_filter_g(), a statistic that
filters-out/filters-in observations in regions of a group with high
density of observations. This statistics is useful for highlighting
observations. It uses a compute_group function. They use internally
MASS:kde2d to estimate densities and default values for
parameters are adjusted dynamically based on the number of
observations.
stat_poly_eq().try_data_frame() to return an object of class
"tibble" and add try_tibble() as synonym.stat_poly_eq().stat_poly_eq().geom_debug().stat_fit_augment().Enhance stat_poly_eq() so that 1) position of labels
according to npc (relative positions using normalized
coordinates), as well as by named positions "top",
"bottom", "right", "left" and
"center" is now implemented; 2) when grouping is present,
suitable vjust values are computed to automatically
position the labels for the different groups without overlap. Default
label positions are now relative to the range of each panel’s \(x\) and \(y\) scales, eliminating in most cases the
need to manually tweak label positions.
Add stat_fit_glance() uses package ‘broom’ for
maximum flexibility in model function choice when wanting to add labels
based on information from a model fit, at the expense of very frequently
having to explicitly set aesthetics, and always having to add code to do
the formatting of the values to be used in labels. Label position is as
described above for stat_poly_eq().
Add stat_fit_deviations() for highlighting residuals
in plots of fitted models. This statistic currently supports only
lm() fits. By default geom “segment” is used to highlight
the deviations of the observations from a fitted model.
Add stat_fit_residuals() for plotting residuals from
a fitted model on their own in plots matching plots of lm fits plotted
with stat_smooth() even with grouping or facets. This statistic
currently supports only lm() fits. By default geom “point”
is used to plot the residual from a fitted model.
Add preliminary version of stat_fit_augment(), which
uses package ‘broom’ for maximum flexibility in model function choice,
to augment the data with additional columns of values derived from a
model fit.
stat_poly_eq().stat_poly_eq().stat_poly_eq().stat_debug_panel() and stat_debug_group() so
that they can optionally print to the console a summary of the data
received as input.geom_debug(), a geom that summarizes its data input
to the console, and produces no visible graphical output.stat_poly_eq().try_data_frame().stat_poly_eq() changed to include the lhs
(left hand side) of the equation by default.Add function try_data_frame() to convert R objects including time
series objects of all classes accepted by try.xts() into
data frames suitable for plotting with ggplot().
Update stat_peaks() and stat_valleys()
to work correctly when the x aesthetic uses a Date or
Datetime continuous scale such as ggplot()
sets automatically for POSIXct variables mapped to the
x aesthetic.
stat_debug() as stat_debug_group()
and add stat_debug_panel().stat_peaks() and stat_valleys() (these
are simpler versions of ggspectra::stat_peaks() and
ggspectra::stat_valleys() for use with any numerical data
(rather than light spectra).First version.
stat_poly_eq()stat_debug()