estimate_r function

Estimates the linear correlation (Pearson's r) between two continuous variables

estimate_r is suitable for a design with two continuous variables. It estimates the linear correlation between two variables (Pearson's r) with a confidence interval. You can pass raw data or summary data.

estimate_r(
  data = NULL,
  x = NULL,
  y = NULL,
  r = NULL,
  n = NULL,
  x_variable_name = "My x variable",
  y_variable_name = "My y variable",
  conf_level = 0.95,
  save_raw_data = TRUE
)

Arguments

• data: For raw data - A data frame 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
• r: For summary data - A pearson's r correlation coefficient
• n: For summary data - Sample size, an integer > 0
• 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.
• 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

Returns

Returns object of class esci_estimate

• overview

  • outcome_variable_name -
  • mean -
  • mean_LL -
  • mean_UL -
  • median -
  • median_LL -
  • median_UL -
  • sd -
  • min -
  • max -
  • q1 -
  • q3 -
  • n -
  • missing -
  • df -
  • mean_SE -
  • median_SE -

• es_r

  • x_variable_name -
  • y_variable_name -
  • effect -
  • effect_size -
  • LL -
  • UL -
  • SE -
  • n -
  • df -
  • ta_LL -
  • ta_UL -

• regression

  • component -
  • values -
  • LL -
  • UL -

• raw_data

  • x -
  • y -
  • fit -
  • lwr -
  • upr -

Details

Reach for this function to conduct simple linear correlation or simple linear regression.

Once you generate an estimate with this function, you can visualize it with plot_correlation() and you can test hypotheses with test_correlation(). In addition, you can use plot_scatter()

to visualize the raw data and to conduct a regression analysis that r returns predicted Y' values from a given X value.

The estimated correlation is from statpsych::ci.cor(), which uses the Fisher r-to-z approach.

Examples

# From raw data
data("data_thomason_1")

estimate_from_raw <- esci::estimate_r(
  esci::data_thomason_1,
  Pretest,
  Posttest
)

# To visualize the value of r
myplot_correlation <- esci::plot_correlation(estimate_from_raw)

# To visualize the data (scatterplot) and use regression to obtain Y' from X
myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10)

# To evaluate a hypothesis (interval null from -0.1 to 0.1):
res_htest_from_raw <- esci::test_correlation(
  estimate_from_raw,
  rope = c(-0.1, 0.1)
)

# From summary data
estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50)

# To visualize the value of r
myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary)

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