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",
  quick = FALSE, p.kr = TRUE, p.adjust.method = "none",
  coefficient.type = NULL, 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, ...)

Arguments

x

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.

output

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).

statistic

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

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; inverse-link transformations (exponentiation or logistic) are not yet implemented, but may be in the future.

conf.method

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".

conf.type

the type of confidence interval (see ordinal::confint.clm())

component

Character specifying whether to tidy the survival or the longitudinal component of the model. Must be either "survival" or "longitudinal". Defaults to "survival".

quick

Logical indiciating if the only the term and estimate columns should be returned. Often useful to avoid time consuming covariance and standard error calculations. Defaults to FALSE.

p.kr

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.

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Default is no correction ("none").

coefficient.type

Relevant only for ordinal regression models (clm , clmm, and polr), this argument decides which parameters to display in the plot. This determines whether parameter 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, if this is NULL, 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, if this argument is NULL, only "coefficient" will be shown. Other option is to show "zeta" parameters.

by.class

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".

effsize

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.

partial

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.

nboot

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.

meta.analytic.effect

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.

point.color

Character describing color for the point (Default: "blue").

point.size

Numeric specifying size for the point (Default: 3).

point.shape

Numeric specifying shape to draw the points (Default: 16 (a dot)).

conf.int

Logical. Decides whether to display confidence intervals as error bars (Default: TRUE).

conf.level

Numeric deciding level of confidence intervals (Default: 0.95). For MCMC model objects (Stan, JAGS, etc.), this will be probability level for CI.

se.type

Character specifying the method used to compute standard standard errors for quantile regression (Default: "nid"). To see all available methods, see quantreg::summary.rq().

k

Number of decimal places expected for results displayed in labels (Default : k = 2).

k.caption.summary

Number of decimal places expected for results displayed in captions (Default : k.caption.summary = 0).

exclude.intercept

Logical that decides whether the intercept should be excluded from the plot (Default: TRUE).

exponentiate

If TRUE, the x-axis will be logarithmic (Default: FALSE).

errorbar.color

Character deciding color of the error bars (Default: "black").

errorbar.height

Numeric specifying the height of the error bars (Default: 0).

errorbar.linetype

Line type of the error bars (Default: "solid").

errorbar.size

Numeric specifying the size of the error bars (Default: 0.5).

vline

Decides whether to display a vertical line (Default: "TRUE").

vline.color

Character specifying color of the vertical line (Default: "black").

vline.linetype

Character specifying line type of the vertical line (Default: "dashed").

vline.size

Numeric specifying the size of the vertical line (Default: 1).

sort

If "none" (default) do not sort, "ascending" sort by increasing coefficient value, or "descending" sort by decreasing coefficient value.

xlab

Label for x axis variable (Default: "estimate").

ylab

Label for y axis variable (Default: "term").

title

The text for the plot title.

subtitle

The text for the plot subtitle. The input to this argument will be ignored if meta.analytic.effect is set to TRUE.

stats.labels

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).

caption

The text for the plot caption.

caption.summary

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.

stats.label.size, stats.label.fontface, stats.label.color

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").

label.r,

Radius of rounded corners, as unit or number. Defaults to 0.15. (Default unit is lines).

label.size

Size of label border, in mm. Defaults to 0.25.

label.box.padding

Amount of padding around bounding box, as number. Defaults to 1. (Default unit is lines).

label.label.padding

Amount of padding around label, as number. Defaults to 0.25. (Default unit is lines).

label.point.padding

Amount of padding around labeled point, as number. Defaults to 0. (Default unit is lines).

label.segment.color

Color of the line segment (Default: "grey50").

label.segment.size

Width of line segment connecting the data point to the text label, in mm. Defaults to 0.5.

label.segment.alpha

Transparency of the line segment. Defaults to the same transparency as the text.

label.min.segment.length

Skip drawing segments shorter than this. Defaults to 0.5. (Default unit is lines).

label.force

Force of repulsion between overlapping text labels. Defaults to 1.

label.max.iter

Maximum number of iterations to try to resolve overlaps. Defaults to 2000.

label.nudge.x, label.nudge.y

Horizontal and vertical adjustments to nudge the starting position of each text label. Defaults to 0.

label.xlim, label.ylim

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).

label.direction

Character ("both", "x", or "y") -- direction in which to adjust position of labels (Default: "y").

package

Name of package from which the palette is desired as string or symbol.

palette

Name of palette as string or symbol.

direction

Either 1 or -1. If -1 the palette will be reversed.

ggtheme

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.).

ggstatsplot.layer

Logical that decides whether theme_ggstatsplot theme elements are to be displayed along with the selected ggtheme (Default: TRUE).

messages

Decides whether messages references, notes, and warnings are to be displayed (Default: TRUE).

...

Additional arguments to tidying method.

Value

Plot with the regression coefficients' point estimates as dots with confidence interval whiskers.

References

https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html

Examples

# 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")
#> # A tibble: 3 x 12 #> term estimate conf.low conf.high std.error statistic p.value significance #> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> #> 1 cyl -1.98 -2.89 -1.06 0.449 -4.40 1.41e-4 *** #> 2 am 10.2 1.36 19.0 4.30 2.36 2.53e-2 * #> 3 cyl:~ -1.31 -2.75 0.143 0.707 -1.85 7.55e-2 ns #> # ... with 4 more variables: p.value.formatted <chr>, p.value.formatted2 <chr>, #> # df.residual <int>, label <chr>
# to get a glance summary ggstatsplot::ggcoefstats(x = mod, output = "glance")
#> # A tibble: 1 x 11 #> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 0.785 0.762 2.94 34.1 1.73e-9 4 -77.8 166. 173. #> # ... with 2 more variables: deviance <dbl>, df.residual <int>
# to get augmented dataframe ggstatsplot::ggcoefstats(x = mod, output = "augment")
#> # A tibble: 32 x 10 #> .rownames mpg cyl am .fitted .resid .std.resid .hat .sigma .cooksd #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 1 21.4 0.364 -0.131 0.106 2.99 5.10e-4 #> 2 Mazda RX4 ~ 21 6 1 21.4 0.364 -0.131 0.106 2.99 5.10e-4 #> 3 Datsun 710 22.8 4 1 27.9 5.13 -1.86 0.117 2.80 1.14e-1 #> 4 Hornet 4 D~ 21.4 6 0 19.0 -2.38 0.842 0.0735 2.96 1.41e-2 #> 5 Hornet Spo~ 18.7 8 0 15.1 -3.63 1.29 0.0784 2.90 3.53e-2 #> 6 Valiant 18.1 6 0 19.0 0.919 -0.325 0.0735 2.99 2.09e-3 #> 7 Duster 360 14.3 8 0 15.1 0.768 -0.272 0.0784 2.99 1.57e-3 #> 8 Merc 240D 24.4 4 0 23.0 -1.43 0.563 0.255 2.98 2.71e-2 #> 9 Merc 230 22.8 4 0 23.0 0.171 -0.0672 0.255 2.99 3.87e-4 #> 10 Merc 280 19.2 6 0 19.0 -0.181 0.0639 0.0735 2.99 8.11e-5 #> # ... with 22 more rows
# -------------- with custom dataframe ----------------------------------- # 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 )
#> Note: No model diagnostics information available for the object of class tbl_df . #>
#>
# -------------- getting model summary ------------------------------ # model library(lme4)
#> Loading required package: Matrix
lmm1 <- lme4::lmer( formula = Reaction ~ Days + (Days | Subject), data = sleepstudy ) # dataframe with model summary ggstatsplot::ggcoefstats(x = lmm1, output = "glance")
#> Computing p-values via Kenward-Roger approximation. Use `p.kr = FALSE` if computation takes too long.
#> # A tibble: 1 x 6 #> sigma logLik AIC BIC REMLcrit df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 25.6 -872. 1756. 1775. 1744. 174
# -------------- 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" )
#> # A tibble: 228 x 13 #> inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 3 306 2 74 1 1 90 100 1175 NA #> 2 3 455 2 68 1 0 90 90 1225 15 #> 3 3 1010 1 56 1 0 90 90 NA 15 #> 4 5 210 2 57 1 1 90 60 1150 11 #> 5 1 883 2 60 1 0 100 90 NA 0 #> 6 12 1022 1 74 1 1 50 80 513 0 #> 7 7 310 2 68 2 2 70 60 384 10 #> 8 11 361 2 71 2 2 60 80 538 1 #> 9 1 218 2 53 1 1 70 80 825 16 #> 10 7 166 2 61 1 2 70 70 271 34 #> # ... with 218 more rows, and 3 more variables: .fitted <dbl>, .se.fit <dbl>, #> # .resid <dbl>