estimate_pdiff_ind_contrast: Estimate the magnitude of difference for an independent...

In rcalinjageman/esci4: Effect Sizes and Confidence Intervals

Estimate the magnitude of difference for an independent groups contrast.

Description

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estimate_mdiff_ind_contrast returns the point estimate and confidence interval for the proportion difference in a linear contrast.

Usage

estimate_pdiff_ind_contrast(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable = NULL,
  cases = NULL,
  ns = NULL,
  contrast = NULL,
  case_label = 1,
  grouping_variable_levels = NULL,
  outcome_variable_name = "My outcome variable",
  grouping_variable_name = "My grouping variable",
  conf_level = 0.95,
  count_NA = FALSE
)

Arguments

data	For raw data - a dataframe or tibble
outcome_variable	For raw data - The column name of the outcome variable, or a vector of numeric data
grouping_variable	For raw data - The column name of the grouping variable, or a vector of group names
cases	For summary data - A vector of 2 or more event counts, each an integer >= 0
ns	For summary data - A vector of sample sizes, same length as counts, each an integer >= correspond event count
contrast	A vector of group weights
case_label	An optional numeric or character label For summary data, used as the label and defaults to 'Affected'. For raw data, used to specify the level used for the proportion.
grouping_variable_levels	For summary data - An optional vector of group labels, same length as means
outcome_variable_name	Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed.
grouping_variable_name	Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed.
conf_level	The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.
count_NA	Logical to count NAs (TRUE) in total N or not (FALSE)

Value

Returns object of class esci_estimate

Examples

# From Summary Data ------------------------------------

estimate_pdiff_ind_contrast(
  cases = c(26, 24, 38),
  ns = c(60, 60, 60),
  contrast = c(1/2, 1/2, -1),
  grouping_variable_levels = c("Therapy", "Drug", "Control")
)

rcalinjageman/esci4 documentation built on May 18, 2023, 4:01 a.m.