data_plot() attempts to extract and tranform an object
to be further plotted, while plot() tries to visualize results of
functions from different packages of the easystats-project.
data_plot(x, data = NULL, ...)# S3 method for see_bayesfactor_models
plot(
x,
n_pies = c("one", "many"),
value = c("none", "BF", "probability"),
sort = FALSE,
log = FALSE,
prior_odds = NULL,
...
)
# S3 method for see_bayesfactor_parameters
plot(
x,
point_size = 2,
rope_color = "#0171D3",
rope_alpha = 0.2,
show_intercept = FALSE,
...
)
# S3 method for see_check_distribution
plot(x, point_size = 2, panel = TRUE, ...)
# S3 method for see_check_normality
plot(x, type = c("density", "qq", "pp"), data = NULL, ...)
# S3 method for see_check_outliers
plot(x, text_size = 3.5, ...)
# S3 method for see_cluster_analysis
plot(x, data = NULL, n_columns = NULL, size = 0.6, ...)
# S3 method for see_compare_performance
plot(x, size = 1, ...)
# S3 method for see_easycorrelation
plot(x, size = 22, text_color = "white", node_color = "#647687", ...)
# S3 method for see_equivalence_test
plot(
x,
rope_color = "#0171D3",
rope_alpha = 0.2,
show_intercept = FALSE,
n_columns = 1,
...
)
# S3 method for see_estimate_density
plot(
x,
stack = TRUE,
show_intercept = FALSE,
n_columns = 1,
priors = FALSE,
priors_alpha = 0.4,
size = 0.9,
...
)
# S3 method for see_hdi
plot(
x,
data = NULL,
show_intercept = FALSE,
show_zero = TRUE,
show_title = TRUE,
n_columns = 1,
...
)
# S3 method for see_n_factors
plot(x, data = NULL, type = c("bar", "line", "area"), size = 1, ...)
# S3 method for see_p_direction
plot(
x,
data = NULL,
show_intercept = FALSE,
priors = FALSE,
priors_alpha = 0.4,
n_columns = 1,
...
)
# S3 method for see_p_significance
plot(
x,
data = NULL,
show_intercept = FALSE,
priors = FALSE,
priors_alpha = 0.4,
n_columns = 1,
...
)
# S3 method for see_parameters_model
plot(
x,
show_intercept = FALSE,
point_size = 0.8,
sort = NULL,
n_columns = NULL,
...
)
# S3 method for see_parameters_pca
plot(
x,
type = c("bar", "line"),
text_size = 3.5,
text_color = "black",
size = 1,
...
)
# S3 method for see_parameters_sem
plot(
x,
data = NULL,
type = c("regression", "correlation", "loading"),
threshold_coefficient = NULL,
threshold_p = NULL,
ci = TRUE,
size = 22,
...
)
# S3 method for see_parameters_simulate
plot(
x,
data = NULL,
stack = TRUE,
show_intercept = FALSE,
n_columns = NULL,
...
)
# S3 method for see_point_estimate
plot(
x,
data = NULL,
point_size = 2,
text_size = 3.5,
panel = TRUE,
show_labels = TRUE,
show_intercept = FALSE,
priors = FALSE,
priors_alpha = 0.4,
...
)
# S3 method for see_rope
plot(
x,
data = NULL,
rope_alpha = 0.5,
rope_color = "cadetblue",
show_intercept = FALSE,
n_columns = 1,
...
)
# S3 method for see_si
plot(
x,
si_color = "#0171D3",
si_alpha = 0.2,
show_intercept = FALSE,
support_only = FALSE,
...
)
An object.
The original data used to create this object. Can be a statistical model or such.
Arguments passed to or from other methods.
Number of pies.
What value to display.
If NULL, coefficients are plotted in the order as they appear in the summary. Use sort = "ascending" (or sort = TRUE)) resp. sort = "descending" to sort coefficients in ascending or descending order.
Sort pie-slices by posterior probability (descending)?
Show log-transformed Bayes factors.
optional vector of prior odds for the models. See
BayesFactor::priorOdds. As the size of the pizza slices corresponds
to posterior probability (which is a function of prior probability and the BF),
custom prior_odds will change the slices' size.
Size of point-geoms.
Color of ROPE/SI ribbon.
Transparency level of ROPE/SI ribbon.
Logical, if TRUE, the intercept-parameter is included
in the plot. By default, it is hidden because in many cases the intercept-parameter
has a posterior distribution on a very different location, so density curves of
posterior distributions for other parameters are hardly visible.
Logical, if TRUE, plots are arranged as panels; else,
single plots are returned.
Character vector, indicating the type of plot (for
check_normality, parameters::model_parameters.lavaan
or n_factors).
Size of text labels.
For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
Size of geoms. Depends on the context of the plot() function,
so this argument may change size of points, lines or bars.
Color of text labels.
Color of node- or circle-geoms.
Logical, if TRUE, densities are plotted as stacked lines.
Else, densities are plotted for each parameter among each other.
Logical, if TRUE, prior distributions are simulated
(using simulate_prior) and added to the plot.
Alpha value of the prior distributions.
Logical, if TRUE, will add a vertical (dotted) line at 0.
Logical, if TRUE, will show the title of the plot.
Numeric, threshold at which value coefficients will be displayed.
Numeric, threshold at which value p-values will be displayed.
Logical, whether confidence intervals should be added to the plot.
Logical, if TRUE, the text labels for the point
estimates (i.e. "Mean", "Median" and/or "MAP") are shown.
You may set show_labels = FALSE in case of overlapping labels, and
add your own legend or footnote to the plot.
Plot only the support data, or show the "raw" prior and posterior distributions? Only applies when plotting si.
data_plot() is in most situation not needed when the purpose
is plotting, since most plot()-functions in see internally call
data_plot() to prepare the data for plotting.
Many plot()-functions have a data-argument that is needed when
the data or model for plotting can't be retrieved via data_plot(). In
such cases, plot() gives an error and asks for providing data or models.
Most plot()-functions work out-of-the-box, i.e. you don't need to do
much more than calling plot(<object>) (see 'Examples'). Some plot-functions
allow to specify arguments to modify the transparancy or color of geoms, these
are shown in the 'Usage' section.
Plot-functions are available for objects from following functions (note that functions from packages might be listed here that are currently still in development and probably not yet available):
bayestestR::bayesfactor_models()
bayestestR::bayesfactor_parameters()
bayestestR::ci()
bayestestR::equivalence_test()
bayestestR::estimate_density()
bayestestR::eti()
bayestestR::hdi()
bayestestR::map_estimate()
bayestestR::p_direction()
bayestestR::p_significance()
bayestestR::point_estimate()
bayestestR::rope()
bayestestR::si()
correlation::correlation()
modelbased::estimate_contrasts()
parameters::cluster_analysis()
parameters::model_parameters()
parameters::n_factors()
parameters::simulate_parameters()
parameters::principal_components()
performance::binned_residuals()
performance::check_collinearity()
performance::check_distribution()
performance::check_heteroscedasticity()
performance::check_homogeneity()
performance::check_model()
performance::check_normality()
performance::check_outliers()
performance::compare_performance()
performance::performance_roc()
# NOT RUN {
library(bayestestR)
if (require("rstanarm")) {
model <- stan_glm(
Sepal.Length ~ Petal.Width * Species,
data = iris,
chains = 2, iter = 200
)
x <- rope(model)
plot(x)
x <- hdi(model)
plot(x) + theme_modern()
data <- rnorm(1000, 1)
x <- p_direction(data)
plot(x)
x <- p_direction(model)
plot(x)
model <- stan_glm(
mpg ~ wt + gear + cyl + disp,
chains = 2,
iter = 200,
data = mtcars
)
x <- equivalence_test(model)
plot(x)
}
# }
# NOT RUN {
library(bayestestR)
library(see)
lm0 <- lm(qsec ~ 1, data = mtcars)
lm1 <- lm(qsec ~ drat, data = mtcars)
lm2 <- lm(qsec ~ wt, data = mtcars)
lm3 <- lm(qsec ~ drat + wt, data = mtcars)
result <- bayesfactor_models(lm1, lm2, lm3, denominator = lm0)
plot(result, n_pies = "one", value = "probability") + theme_modern() +
scale_fill_pizza(reverse = TRUE)
# }
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