Function takes a regression model object and converts it to a ARD
structure using the broom.helpers package.
ard_regression(x, ...)
# Default S3 method
ard_regression(x, tidy_fun = broom.helpers::tidy_with_broom_or_parameters, ...)
# S3 method for class 'data.frame'
ard_regression(
x,
formula,
method,
method.args = list(),
package = "base",
tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
...
)(regression model/data.frame)
regression model object or a data frame
Arguments passed to broom.helpers::tidy_plus_plus()
(function)
a tidier. Default is broom.helpers::tidy_with_broom_or_parameters
(formula)
a formula
(string)
string of function naming the function to be called, e.g. "glm".
If function belongs to a library that is not attached, the package name
must be specified in the package argument.
(named list)
named list of arguments that will be passed to method.
Note that this list may contain non-standard evaluation components.
If you are wrapping this function in other functions, the argument
must be passed in a way that does not evaluate the list, e.g.
using rlang's embrace operator {{ . }}.
(string)
a package name that will be temporarily loaded when function
specified in method is executed.
data frame
lm(AGE ~ ARM, data = cards::ADSL) |>
ard_regression(add_estimate_to_reference_rows = TRUE)
#> {cards} data frame: 43 x 9
#> variable variable_level context stat_name stat_label stat
#> 1 ARM Placebo regressi… term term ARMPlace…
#> 2 ARM Placebo regressi… var_label Label Descript…
#> 3 ARM Placebo regressi… var_class Class character
#> 4 ARM Placebo regressi… var_type Type categori…
#> 5 ARM Placebo regressi… var_nlevels N Levels 3
#> 6 ARM Placebo regressi… contrasts contrasts contr.tr…
#> 7 ARM Placebo regressi… contrasts_type Contrast… treatment
#> 8 ARM Placebo regressi… reference_row referenc… TRUE
#> 9 ARM Placebo regressi… label Level La… Placebo
#> 10 ARM Placebo regressi… n_obs N Obs. 86
#> ℹ 33 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 3 more variables: fmt_fun, warning, error
ard_regression(
x = cards::ADSL,
formula = AGE ~ ARM,
method = "lm"
)
#> {cards} data frame: 43 x 9
#> variable variable_level context stat_name stat_label stat
#> 1 ARM Placebo regressi… term term ARMPlace…
#> 2 ARM Placebo regressi… var_label Label Descript…
#> 3 ARM Placebo regressi… var_class Class character
#> 4 ARM Placebo regressi… var_type Type categori…
#> 5 ARM Placebo regressi… var_nlevels N Levels 3
#> 6 ARM Placebo regressi… contrasts contrasts contr.tr…
#> 7 ARM Placebo regressi… contrasts_type Contrast… treatment
#> 8 ARM Placebo regressi… reference_row referenc… TRUE
#> 9 ARM Placebo regressi… label Level La… Placebo
#> 10 ARM Placebo regressi… n_obs N Obs. 86
#> ℹ 33 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 3 more variables: fmt_fun, warning, error