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Model coefficients for fitted models with the model summary as a caption.
ggcoefstats(x, output = "plot", statistic = NULL, scales = NULL,
conf.method = "Wald", conf.type = "Wald", component = "survival",
bf.message = TRUE, d = "norm", d.par = c(mean = 0, sd = 0.3),
tau = "halfcauchy", tau.par = c(scale = 0.5), iter = 5000,
summarize = "stan", p.kr = TRUE, p.adjust.method = "none",
coefficient.type = c("beta", "location", "coefficient"),
by.class = FALSE, effsize = "eta", partial = TRUE, nboot = 500,
meta.analytic.effect = FALSE, point.color = "blue", point.size = 3,
point.shape = 16, conf.int = TRUE, conf.level = 0.95,
se.type = "nid", k = 2, k.caption.summary = 0,
exclude.intercept = TRUE, exponentiate = FALSE,
errorbar.color = "black", errorbar.height = 0,
errorbar.linetype = "solid", errorbar.size = 0.5, vline = TRUE,
vline.color = "black", vline.linetype = "dashed", vline.size = 1,
sort = "none", xlab = "regression coefficient", ylab = "term",
title = NULL, subtitle = NULL, stats.labels = TRUE,
caption = NULL, caption.summary = TRUE, stats.label.size = 3,
stats.label.fontface = "bold", stats.label.color = NULL,
label.r = 0.15, label.size = 0.25, label.box.padding = 1,
label.label.padding = 0.25, label.point.padding = 0.5,
label.segment.color = "grey50", label.segment.size = 0.5,
label.segment.alpha = NULL, label.min.segment.length = 0.5,
label.force = 1, label.max.iter = 2000, label.nudge.x = 0,
label.nudge.y = 0, label.xlim = c(NA, NA), label.ylim = c(NA, NA),
label.direction = "y", package = "RColorBrewer", palette = "Dark2",
direction = 1, ggtheme = ggplot2::theme_bw(),
ggstatsplot.layer = TRUE, messages = FALSE, return = NULL, ...)A model object to be tidied with broom::tidy, or a tidy data frame
containing results. If a data frame is to be plotted, it must contain
columns named term (names of predictors), or estimate (corresponding
estimates of coefficients or other quantities of interest). Other optional
columns are conf.low and conf.high (for confidence intervals);
p.value. It is important that all term names should be unique.
Character describing the expected output from this function:
"plot" (visualization of regression coefficients) or "tidy" (tidy
dataframe of results from broom::tidy) or "glance" (object from
broom::glance) or "augment" (object from broom::augment).
Which statistic is to be displayed (either "t" or "f"or
"z") in the label. This is especially important if the x argument in
ggcoefstats is a dataframe in which case the function wouldn't know what
kind of model it is dealing with.
scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"vcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects.
Character describing method for computing confidence
intervals (for more, see ?lme4::confint.merMod and
?broom.mixed::tidy.brmsfit). This argument has different defaults
depending on the model object. For the merMod class model objects
(lmer, glmer, nlmer, etc.), the default is "Wald" (other options
are: "profile", "boot"). For MCMC or brms fit model objects (Stan,
JAGS, etc.), the default is "quantile", while the only other options is
"HPDinterval".
Whether to use "profile" or "Wald" confidendence
intervals, passed to the type argument of ordinal::confint.clm().
Defaults to "profile".
Character specifying whether to tidy the survival or
the longitudinal component of the model. Must be either "survival" or
"longitudinal". Defaults to "survival".
Logical that decides whether results from running a
Bayesian meta-analysis assuming that the effect size d varies across
studies with standard deviation t (i.e., a random-effects analysis)
should be displayed in caption. Defaults to TRUE.
the prior distribution of the average effect size "norm") or via
prior.
prior parameters for d specifies
the type of family).
the prior distribution of the between-study heterogeneity
"halfcauchy") or via prior.
prior parameters for tau
specifies the type of family).
number of MCMC iterations using Stan.
how to estimate parameter summaries (mean, median, SD,
etc.): Either by numerical integration (summarize = "integrate") or
based on MCMC/Stan samples (summarize = "stan").
Logical, if TRUE, the computation of p-values for lmer is
based on conditional F-tests with Kenward-Roger approximation for the df.
For details, see ?sjstats::p_value.
Adjustment method for p-values for multiple
comparisons. Possible methods are: "holm", "hochberg", "hommel",
"bonferroni", "BH", "BY", "fdr", "none". Default is no correction
("none"). This argument is relevant for multiplicity correction for
multiway ANOVA designs (see,
Cramer
et al., 2015).
Relevant only for ordinal regression models (clm ,
clmm, "svyolr", and polr), this argument decides which parameters are
display in the plot. Available parameters are: parameter that measures the
intercept, i.e. the log-odds distance between response values
("alpha"); effects on the location ("beta"); or effects on the
scale ("zeta"). For clm and clmm models, by default, only
"beta" (a vector of regression parameters) parameters will be show. Other
options are "alpha" (a vector of threshold parameters) or "both". For
polr models, by default, only "coefficient" will be shown. Other option
is to show "zeta" parameters. Note that, from broom 0.7.0 onward,
coefficients will be renamed and "intercept" type coefficients will
correspond to "alpha" parameters, "location" type coefficients will
correspond to "beta" parameters, and "scale" type coefficients will
correspond to "zeta" parameters.
A logical indicating whether or not to show performance
measures broken down by class. Defaults to FALSE. When by.class = FALSE
only returns a tibble with accuracy and kappa statistics. Mostly relevant
for an object of class "confusionMatrix".
Character describing the effect size to be displayed: "eta"
(default) or "omega". This argument is relevant
only for models objects of class aov, anova, and aovlist.
Logical that decides if partial eta-squared or omega-squared
are returned (Default: TRUE). If FALSE, eta-squared or omega-squared
will be returned. Valid only for objects of class aov, anova, or
aovlist.
Number of bootstrap samples for confidence intervals for partial
eta-squared and omega-squared (Default: 500). This argument is relevant
only for models objects of class aov, anova, and aovlist.
Logical that decides whether subtitle for
meta-analysis via linear (mixed-effects) models - as implemented in the
metafor package - is to be displayed (default: FALSE). If TRUE, input
to argument subtitle will be ignored. This will be mostly relevant if a
data frame with estimates and their standard errors is entered as input to
x argument.
Character describing color for the point (Default:
"blue").
Numeric specifying size for the point (Default: 3).
Numeric specifying shape to draw the points (Default: 16
(a dot)).
Logical. Decides whether to display confidence intervals as
error bars (Default: TRUE).
Numeric deciding level of confidence intervals (Default:
0.95). For MCMC model objects (Stan, JAGS, etc.), this will be
probability level for CI.
Character specifying the method used to compute standard
standard errors for quantile regression (Default: "nid"). To see all
available methods, see quantreg::summary.rq().
Number of decimal places expected for results displayed in labels
(Default : k = 2).
Number of decimal places expected for results
displayed in captions (Default : k.caption.summary = 0).
Logical that decides whether the intercept should be
excluded from the plot (Default: TRUE).
If TRUE, the x-axis will be logarithmic (Default:
FALSE).
Character deciding color of the error bars (Default:
"black").
Numeric specifying the height of the error bars
(Default: 0).
Line type of the error bars (Default: "solid").
Numeric specifying the size of the error bars (Default:
0.5).
Decides whether to display a vertical line (Default: "TRUE").
Character specifying color of the vertical line (Default:
"black").
Character specifying line type of the vertical line
(Default: "dashed").
Numeric specifying the size of the vertical line (Default:
1).
If "none" (default) do not sort, "ascending" sort by
increasing coefficient value, or "descending" sort by decreasing
coefficient value.
Label for x axis variable (Default: "estimate").
Label for y axis variable (Default: "term").
The text for the plot title.
The text for the plot subtitle. The input to this argument
will be ignored if meta.analytic.effect is set to TRUE.
Logical. Decides whether the statistic and p-values for
each coefficient are to be attached to each dot as a text label using
ggrepel (Default: TRUE).
The text for the plot caption.
Logical. Decides whether the model summary should be
displayed as a cation to the plot (Default: TRUE). Color of the line
segment. Defaults to the same color as the text.
Aesthetics for
the labels. Defaults: 3, "bold",NULL, resp. If stats.label.color is
NULL, colors will be chosen from the specified package (Default:
"RColorBrewer") and palette (Default: "Dark2").
Radius of rounded corners, as unit or number. Defaults to
0.15. (Default unit is lines).
Size of label border, in mm. Defaults to 0.25.
Amount of padding around bounding box, as number.
Defaults to 1. (Default unit is lines).
Amount of padding around label, as number.
Defaults to 0.25. (Default unit is lines).
Amount of padding around labeled point, as
number. Defaults to 0. (Default unit is lines).
Color of the line segment (Default: "grey50").
Width of line segment connecting the data point to
the text label, in mm. Defaults to 0.5.
Transparency of the line segment. Defaults to the same transparency as the text.
Skip drawing segments shorter than this.
Defaults to 0.5. (Default unit is lines).
Force of repulsion between overlapping text labels.
Defaults to 1.
Maximum number of iterations to try to resolve
overlaps. Defaults to 2000.
Horizontal and vertical adjustments to
nudge the starting position of each text label. Defaults to 0.
Limits for the x and y axes. Text labels will be
constrained to these limits. By default, text labels are constrained to the
entire plot area. Defaults to c(NA, NA).
Character ("both", "x", or "y") -- direction in
which to adjust position of labels (Default: "y").
Name of package from which the palette is desired as string or symbol.
Name of palette as string or symbol.
Either 1 or -1. If -1 the palette will be reversed.
A function, ggplot2 theme name. Default value is
ggplot2::theme_bw(). Any of the ggplot2 themes, or themes from
extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(),
hrbrthemes::theme_ipsum_ps(), etc.).
Logical that decides whether theme_ggstatsplot
theme elements are to be displayed along with the selected ggtheme
(Default: TRUE).
Decides whether messages references, notes, and warnings are
to be displayed (Default: TRUE).
Additional arguments to tidying method.
Plot with the regression coefficients' point estimates as dots with confidence interval whiskers.
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html
# NOT RUN {
# for reproducibility
set.seed(123)
# -------------- with model object --------------------------------------
# model object
mod <- lm(formula = mpg ~ cyl * am, data = mtcars)
# to get a plot
ggstatsplot::ggcoefstats(x = mod, output = "plot")
# to get a tidy dataframe
ggstatsplot::ggcoefstats(x = mod, output = "tidy")
# to get a glance summary
ggstatsplot::ggcoefstats(x = mod, output = "glance")
# to get augmented dataframe
ggstatsplot::ggcoefstats(x = mod, output = "augment")
# -------------- with custom dataframe -----------------------------------
# }
# NOT RUN {
# creating a dataframe
df <-
structure(
list(
term = structure(
c(3L, 4L, 1L, 2L, 5L),
.Label = c(
"Africa",
"Americas", "Asia", "Europe", "Oceania"
),
class = "factor"
),
estimate = c(
0.382047603321706,
0.780783111514665,
0.425607573765058,
0.558365541235078,
0.956473848429961
),
std.error = c(
0.0465576338644502,
0.0330218199731529,
0.0362834986178494,
0.0480571500648261,
0.062215818388157
),
statistic = c(
8.20590677855356,
23.6444603038067,
11.7300588415607,
11.6187818146078,
15.3734833553524
),
conf.low = c(
0.290515146096969,
0.715841986960399,
0.354354575031406,
0.46379116008131,
0.827446138277154
),
conf.high = c(
0.473580060546444,
0.845724236068931,
0.496860572498711,
0.652939922388847,
1.08550155858277
),
p.value = c(
3.28679518728519e-15,
4.04778497135963e-75,
7.59757330804449e-29,
5.45155840151592e-26,
2.99171217913312e-13
),
df.residual = c(
394L, 358L, 622L,
298L, 22L
)
),
row.names = c(NA, -5L),
class = c(
"tbl_df",
"tbl", "data.frame"
)
)
# plotting the dataframe
ggstatsplot::ggcoefstats(
x = df,
statistic = "t",
meta.analytic.effect = TRUE,
k = 3
)
# }
# NOT RUN {
# -------------- getting model summary ------------------------------
# model
library(lme4)
lmm1 <- lme4::lmer(
formula = Reaction ~ Days + (Days | Subject),
data = sleepstudy
)
# dataframe with model summary
ggstatsplot::ggcoefstats(x = lmm1, output = "glance")
# -------------- getting augmented dataframe ------------------------------
# setup
set.seed(123)
library(survival)
# fit
cfit <-
survival::coxph(formula = Surv(time, status) ~ age + sex, data = lung)
# augmented dataframe
ggstatsplot::ggcoefstats(
x = cfit,
data = lung,
output = "augment",
type.predict = "risk"
)
# }
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