tm_a_regression {teal.modules.general} | R Documentation |
teal
module: Scatterplot and regression analysis
Description
Module for visualizing regression analysis, including scatterplots and various regression diagnostics plots. It allows users to explore the relationship between a set of regressors and a response variable, visualize residuals, and identify outliers.
Usage
tm_a_regression(
label = "Regression Analysis",
regressor,
response,
plot_height = c(600, 200, 2000),
plot_width = NULL,
alpha = c(1, 0, 1),
size = c(2, 1, 8),
ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
ggplot2_args = teal.widgets::ggplot2_args(),
pre_output = NULL,
post_output = NULL,
default_plot_type = 1,
default_outlier_label = "USUBJID",
label_segment_threshold = c(0.5, 0, 10),
transformators = list(),
decorators = list()
)
Arguments
label |
( |
regressor |
( |
response |
( |
plot_height |
( |
plot_width |
( |
alpha |
(
|
size |
(
|
ggtheme |
( |
ggplot2_args |
( List names should match the following: For more details see the vignette: |
pre_output |
( |
post_output |
( |
default_plot_type |
(
|
default_outlier_label |
( |
label_segment_threshold |
( It can take the following forms:
|
transformators |
( |
decorators |
See section "Decorating Module" below for more details. |
Value
Object of class teal_module
to be used in teal
applications.
Decorating Module
This module generates the following objects, which can be modified in place using decorators:
-
plot
(ggplot
)
A Decorator is applied to the specific output using a named list of teal_transform_module
objects.
The name of this list corresponds to the name of the output to which the decorator is applied.
See code snippet below:
tm_a_regression( ..., # arguments for module decorators = list( plot = teal_transform_module(...) # applied to the `plot` output ) )
For additional details and examples of decorators, refer to the vignette
vignette("decorate-module-output", package = "teal.modules.general")
.
To learn more please refer to the vignette
vignette("transform-module-output", package = "teal")
or the teal::teal_transform_module()
documentation.
Examples in Shinylive
- example-1
- example-2
Note
For more examples, please see the vignette "Using regression plots" via
vignette("using-regression-plots", package = "teal.modules.general")
.
Examples
# general data example
data <- teal_data()
data <- within(data, {
require(nestcolor)
CO2 <- CO2
})
app <- init(
data = data,
modules = modules(
tm_a_regression(
label = "Regression",
response = data_extract_spec(
dataname = "CO2",
select = select_spec(
label = "Select variable:",
choices = "uptake",
selected = "uptake",
multiple = FALSE,
fixed = TRUE
)
),
regressor = data_extract_spec(
dataname = "CO2",
select = select_spec(
label = "Select variables:",
choices = variable_choices(data[["CO2"]], c("conc", "Treatment")),
selected = "conc",
multiple = TRUE,
fixed = FALSE
)
)
)
)
)
if (interactive()) {
shinyApp(app$ui, app$server)
}
# CDISC data example
data <- teal_data()
data <- within(data, {
require(nestcolor)
ADSL <- teal.data::rADSL
})
join_keys(data) <- default_cdisc_join_keys[names(data)]
app <- init(
data = data,
modules = modules(
tm_a_regression(
label = "Regression",
response = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variable:",
choices = "BMRKR1",
selected = "BMRKR1",
multiple = FALSE,
fixed = TRUE
)
),
regressor = data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variables:",
choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
selected = "AGE",
multiple = TRUE,
fixed = FALSE
)
)
)
)
)
if (interactive()) {
shinyApp(app$ui, app$server)
}