...) to use_label() so that in addition to a character vector of label names passed to parameter labels, separate strings for each name are now also accepted.stat_poly_eq(), stat_quant_eq() and stat_ma_eq() (not in the input!).stat_quant_eq() return new qtl.label for quantiles, separately from grp.label, which now works only based on a pseudo aesthetic, as in stat_poly_eq() and stat_ma_eq(). This is a soft code breaking change affecting the rendering of model equation labels in some plots.check_poly_formula() and use it in stat_poly_eq() and stat_quant_eq() to validate that the user-supplied model formula is a polynomial with terms in order of increasing powers, except when output.type = "numeric". If not validated, NA is returned as formatted character string for the equation label.stat_poly_eq(), stat_ma_eq(), stat_quant_eq(), stat_correlation(), and stat_multcomp() were in part reimplemented, introducing small visual changes in the formatting of labels, including changes in the default number of digits in very few cases.stat_poly_eq(), stat_ma_eq(), stat_quant_eq(), stat_correlation(), and stat_multcomp().ggpmisc.small.p, ggpmisc.small.r and ggpmisc.decreasing.poly.eq. Obey R option "verbose" by displaying additional informative messages.stat_poly_eq(), stat_poly_line(), stat_ma_eq(), stat_ma_line(), stat_quant_eq(), stat_quant_line(), and stat_quant_band() to return an atomic NA or a length zero object to skip labelling or plotting related to the attempted model fit.label.x.npc and label.y.npc have been removed. This is a code breaking change, preceded by a long deprecation period. To update broken code, simply replace label.y.npc by label.y and label.x.npc by label.x in the call still passing the same arguments.trans of scale_y_Pvalue() into transform to track deprecation in 'ggplot2' 3.5.0.stat_multcomp() to flexibly include in labels, both when using "bars" and "letters", the abbreviated name of the method used to adjust P-values (suggested by markbneal, Mark Neal).stat_multcomp() to flexibly include only when using "letters" an additional label with the critical P-value and the method used to adjust the empirical P-values (suggested by markbneal, Mark Neal).stat_multcomp() to support arbitrary sets of pairwise contrasts.stat_multcomp(): adjusted.type to p.adjust.method, and constrast.type to contrasts.p.adjust.method so that they depend on the argument passed to contrasts.stat_correlation(), stat_poly_eq(), stat_ma_eq(), and stat_multcomp(), p.digits = Inf as a request to use scientific notation for p.value.label (suggested by wbvguo, Wenbin Guo).stat_multcomp(): wrongly encoded letters in Tukey contrasts in case of grouping factors with more than nine levels.stat_multcomp(): warning issued by mvtnorm::pmvnorm() because of convergence failure in Tukey contrasts in case of grouping factors with more than approximately 5 to 7 levels. Convergence failure also meant slightly different P-values returned in different runs in these cases.stat_multcomp(): off-plot letter labels with Tukey contrasts in some plots with more than five groups.orientation from formula to allow function calls on the
rhs of formulas, e.g., I(y - 10) ~ x or I(x - 10) ~ y.find_peaks() which was previously internal.stat_multcomp() that computes adjusted p-values and constructs labels to annotate plots with results from multiple comparisons based on "Tukey" or "Dunnet" contrasts.stat_correlation() are no longer computed by default for methods other than Pearson, as when
using bootstrap the computation can be time-consuming and occasionally
fail. Previous default can be restored by passing 0.95 as argument to
r.conf.level.stat_ma_line(): error triggered when returned values from computation of confidence band are missing. (Reported by rakelrpf as issue #36.)stat_quant_eq(): rho.label showed the numeric value of AIC instead of rho.stat_poly_eq() (and other statistics) with package 'gganimate' (Bug reported by EvoLandEco as issue #38).stat_poly_eq(), stat_ma_eq() and stat_quant_eq() (Use case from EvoLandEco in issue #38.).n.min to all statistics that fit a model or compute correlations. Default value is the previously hard-coded value, except in one case where the previously hard-coded value was wrong.stat_fit_fitted().base::isa() which is not supported for "formula" in R < 4.1.0 (reported by Johnny Le).stat_peaks() and stat_valleys() that made peak and valley labels for datetime variables mapped to x to be always formatted in the local system's timezone instead of in the timezone of the x scale of the ggplot.stat_poly_eq(), stat_ma_eq(), stat_quant_eq(), and stat_correlation() that caused some labels not to obey R option OutDec. (Problem described at Stackoverflow.)formula() method on fitted model but fall-back onto the 'formula' argument in case of error or return NA if everything fails, without triggering an error condition.fm.tb.type, fm.class, fm.method, fm.formula, and fm.formula.chr in the data returned by stat_fit_tb(), and rename mf_tb into fm.tb for naming consistency.fm.formula in the data returned by all other textual-annotation statistics based on model fitting.fm.class, fm.method, and fm.formula.chr in addition to fm.formula in the data returned by line plotting statistics based on model fitting when passed argument fm.values = TRUE.scale_colour_logFC(), scale_color_logFC() and scale_fill_logFC().scale_colour_outcome() and scale_fill_outcome() adding flexibility to the value names and allowing a work-around for non-functional drop in manual scales due to a bug present in 'ggplot2' (only in versions 3.3.4, 3.3.5, 3.3.6).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 textual-annotation statistics 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()