estimate_rdiff_two: Estimate a correlation.

In rcalinjageman/esci4: Effect Sizes and Confidence Intervals

Estimate a correlation.

Description

Returns effect sizes appropriate for estimating the linear relationship between two quantitative variables

Usage

estimate_rdiff_two(
  data = NULL,
  x = NULL,
  y = NULL,
  grouping_variable = NULL,
  comparison_r = NULL,
  comparison_n = NULL,
  reference_r = NULL,
  reference_n = NULL,
  grouping_variable_levels = NULL,
  x_variable_name = "My x variable",
  y_variable_name = "My y variable",
  grouping_variable_name = "My grouping variable",
  conf_level = 0.95,
  save_raw_data = TRUE
)

Arguments

data	For raw data - a dataframe or tibble
x	For raw data - The column name of the outcome variable, or a vector of numeric data
y	For raw data - The column name of the outcome variable, or a vector of numeric data
grouping_variable	For raw data, a vector that is a factor or the name of a factor column from data
comparison_r	For summary data, a pearson's r correlation coefficient
comparison_n	For summary data - An integer > 0
reference_r	For summary data, a pearson's r correlation coefficient
reference_n	For summary data - An integer > 0
grouping_variable_levels	For summary data - An optional vector of 2 group labels
x_variable_name	Optional friendly name for the x variable. Defaults to 'My x variable' or the outcome variable column name if a data frame is passed.
y_variable_name	Optional friendly name for the y variable. Defaults to 'My y 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.
save_raw_data	For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object

Value

Returnsobject of class esci_estimate

Examples

# From Raw Data ------------------------------------
# Just pass in the data source, grouping column, and outcome column.
# You can pass these in by position, skipping the labels:

# Note... not sure if PlantGrowth dataset meets assumptions for this analysis
estimate_correlation(
 datasets::anscombe,
 x1,
 y1
)

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