Estimates the difference in correlation for a design with two groups and two continuous outcome variables

Returns object estimate_rdiff_two is suitable for a simple two-group design with two continuous outcome variables where you want to estimate the difference in the strength of the relationship between the two groups. It estimate the linear correlation (Pearson's r) for each group and the difference in r, along with confidence intervals. You can pass raw data or summary data.

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

Returns object of class esci_estimate

overview
- outcome_variable_name -
- grouping_variable_name -
- grouping_variable_level -
- mean -
- mean_LL -
- mean_UL -
- median -
- median_LL -
- median_UL -
- sd -
- min -
- max -
- q1 -
- q3 -
- n -
- missing -
- df -
- mean_SE -
- median_SE -
es_r_difference
- type -
- grouping_variable_name -
- grouping_variable_level -
- x_variable_name -
- y_variable_name -
- effect -
- effect_size -
- LL -
- UL -
- SE -
- n -
- df -
- ta_LL -
- ta_UL -
- rz -
- sem -
- z -
- p -
es_r
- grouping_variable_name -
- grouping_variable_level -
- x_variable_name -
- y_variable_name -
- effect -
- effect_size -
- LL -
- UL -
- SE -
- n -
- df -
- ta_LL -
- ta_UL -
raw_data
- x -
- y -
- grouping_variable -

Details

Once you generate an estimate with this function, you can visualize it with plot_rdiff() and you can test hypotheses with test_rdiff(). In addition, you can use plot_scatter() to visualize the raw data.

The estimated single-group r values are from statpsych::ci.cor().

The difference in r values is from statpsych::ci.cor2().

Examples

# From raw data
data("data_campus_involvement")

estimate_from_raw <- esci::estimate_rdiff_two(
  esci::data_campus_involvement,
  GPA,
  SWB,
  Gender
)

# To visualize the difference in r
myplot_from_raw <- esci::plot_rdiff(estimate_from_raw)
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.

# To visualize the data (scatterplot) by group
myplot_scatter <- esci::plot_scatter(estimate_from_raw)

# To evaluate a hypothesis (by default: point null of exaclty 0):
res_htest_from_raw <- esci::test_rdiff(
  estimate_from_raw
)


# From summary data
estimate <- esci::estimate_rdiff_two(
  comparison_r = .53,
  comparison_n = 45,
  reference_r = .41,
  reference_n = 59,
  grouping_variable_levels = c("Females", "Males"),
  x_variable_name = "Satisfaction with life",
  y_variable_name = "Body satisfaction",
  grouping_variable_name = "Gender",
  conf_level = .95
)

myplot_from_summary <- esci::plot_rdiff(estimate)
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.

# To evaluate a hypothesis (interval null from -0.1 to 0.1):
res_htest_from_summary <- esci::test_rdiff(
  estimate,
  rope = c(-0.1, 0.1)
)