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.

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

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 -

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)
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning: All aesthetics have length 1, but the data has 12 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.

# 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)
)