estimate_r {esci}

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

Description

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.

Usage

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

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.

Value

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 -

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)
)