esci (version 1.0.7)

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

Description

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
)

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 -

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

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)

# 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)

# 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)
)