ARD for LS Mean Difference — ard_emmeans_mean

This function calculates least-squares mean differences using the 'emmeans' package using the following

emmeans::emmeans(object = <regression model>, specs = ~ <primary covariate>) |>
  emmeans::contrast(method = "pairwise") |>
  summary(infer = TRUE, level = <confidence level>)

The arguments data, formula, method, method.args, package are used to construct the regression model via cardx::construct_model().

Usage

ard_emmeans_mean_difference(
  data,
  formula,
  method,
  method.args = list(),
  package = "base",
  response_type = c("continuous", "dichotomous"),
  conf.level = 0.95,
  primary_covariate = getElement(attr(stats::terms(formula), "term.labels"), 1L)
)

Arguments

data

(data.frame/survey.design)
a data frame or survey design object

formula

(formula)
a formula

method

(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.

method.args

(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 {{ . }}.

package

(string)
string of package name that will be temporarily loaded when function specified in method is executed.

response_type

(string) string indicating whether the model outcome is 'continuous' or 'dichotomous'. When 'dichotomous', the call to emmeans::emmeans() is supplemented with argument regrid="response".

conf.level

(scalar numeric)
confidence level for confidence interval. Default is 0.95.

primary_covariate

(string)
string indicating the primary covariate (typically the dichotomous treatment variable). Default is the first covariate listed in the formula.

Value

ARD data frame

Examples

ard_emmeans_mean_difference(
  data = mtcars,
  formula = mpg ~ am + cyl,
  method = "lm"
)
#> {cards} data frame: 8 x 10
#>   group1 variable variable_level  stat_name stat_label      stat
#> 1     am contrast      am0 - am1   estimate  Mean Dif…    -2.567
#> 2     am contrast      am0 - am1  std.error  std.error     1.291
#> 3     am contrast      am0 - am1         df         df        29
#> 4     am contrast      am0 - am1   conf.low  CI Lower…    -5.208
#> 5     am contrast      am0 - am1  conf.high  CI Upper…     0.074
#> 6     am contrast      am0 - am1    p.value    p-value     0.056
#> 7     am contrast      am0 - am1 conf.level  CI Confi…      0.95
#> 8     am contrast      am0 - am1     method     method Least-sq…
#> ℹ 4 more variables: context, fmt_fn, warning, error

ard_emmeans_mean_difference(
  data = mtcars,
  formula = vs ~ am + mpg,
  method = "glm",
  method.args = list(family = binomial),
  response_type = "dichotomous"
)
#> {cards} data frame: 8 x 10
#>   group1 variable variable_level  stat_name stat_label      stat
#> 1     am contrast      am0 - am1   estimate  Mean Dif…      0.61
#> 2     am contrast      am0 - am1  std.error  std.error     0.229
#> 3     am contrast      am0 - am1         df         df       Inf
#> 4     am contrast      am0 - am1   conf.low  CI Lower…     0.162
#> 5     am contrast      am0 - am1  conf.high  CI Upper…     1.059
#> 6     am contrast      am0 - am1    p.value    p-value     0.008
#> 7     am contrast      am0 - am1 conf.level  CI Confi…      0.95
#> 8     am contrast      am0 - am1     method     method Least-sq…
#> ℹ 4 more variables: context, fmt_fn, warning, error