junco (version 0.1.2)

a_summarize_ancova_j: ANCOVA Summary Function

Description

Combination of tern::s_summary, and ANCOVA based estimates for mean and diff between columns, based on ANCOVA function s_ancova_j.

Usage

a_summarize_ancova_j(
  df,
  .var,
  .df_row,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
s_summarize_ancova_j(df, .var, .df_row, .ref_group, .in_ref_col, ...)

Value

• a_summarize_ancova_j() returns the corresponding list with formatted rtables::CellValue().

returns the statistics from tern::s_summary(x), appended with a new statistics based upon ANCOVA

Arguments

df

(data.frame)
data set containing all analysis variables.

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

.df_row

(data.frame)
data set that includes all the variables that are called in .var and variables.

ref_path

(character)
path to the reference group.

.spl_context

(environment)
split context environment.

...

Additional arguments passed to s_ancova_j.

.stats

(character)
statistics to calculate.

.formats

(list)
formats for the statistics.

.labels

(list)
labels for the statistics.

.indent_mods

(list)
indentation modifications for the statistics.

.ref_group

(data.frame or vector)
the data corresponding to the reference group.

.in_ref_col

(flag)
TRUE when working with the reference level, FALSE otherwise.

Functions

• a_summarize_ancova_j(): Formatted analysis function which is used as afun. Note that the junco specific ref_path and .spl_context arguments are used for reference column information.

Details

Combination of tern::s_summary, and ANCOVA based estimates for mean and diff between columns, based on ANCOVA function s_ancova_j

See Also

Other Inclusion of ANCOVA Functions: a_summarize_aval_chg_diff_j(), s_ancova_j()

Examples

basic_table() |>
  split_cols_by("Species") |>
  add_colcounts() |>
  analyze(
    vars = "Petal.Length",
    afun = a_summarize_ancova_j,
    show_labels = "hidden",
    na_str = tern::default_na_str(),
    table_names = "unadj",
    var_labels = "Unadjusted comparison",
    extra_args = list(
      variables = list(arm = "Species", covariates = NULL),
      conf_level = 0.95,
      .labels = c(lsmean = "Mean", lsmean_diff = "Difference in Means"),
      ref_path = c("Species", "setosa")
    )
  ) |>
  analyze(
    vars = "Petal.Length",
    afun = a_summarize_ancova_j,
    show_labels = "hidden",
    na_str = tern::default_na_str(),
    table_names = "adj",
    var_labels = "Adjusted comparison (covariates: Sepal.Length and Sepal.Width)",
    extra_args = list(
      variables = list(
        arm = "Species",
        covariates = c("Sepal.Length", "Sepal.Width")
      ),
      conf_level = 0.95,
      ref_path = c("Species", "setosa")
    )
  ) |>
  build_table(iris)

library(dplyr)
library(tern)

df <- iris |> filter(Species == "virginica")
.df_row <- iris
.var <- "Petal.Length"
variables <- list(arm = "Species", covariates = "Sepal.Length * Sepal.Width")
.ref_group <- iris |> filter(Species == "setosa")
conf_level <- 0.95
s_summarize_ancova_j(
  df,
  .var = .var,
  .df_row = .df_row,
  variables = variables,
  .ref_group = .ref_group,
  .in_ref_col = FALSE,
  conf_level = conf_level
)