neatStats (version 1.13.3)

corr_neat: Correlation Statistics

Description

Pearson correlation results including confidence interval (CI) and correlation Bayes factor (BF). For non-parametric version, Spearman's rank correlation results along with corresponding rank-based BFs (as per van Doorn et al., 2020).

Usage

corr_neat(
  var1,
  var2,
  nonparametric = FALSE,
  ci = 0.95,
  bf_added = FALSE,
  direction = NULL,
  round_r = 3,
  for_table = FALSE,
  sb_correction = FALSE,
  hush = FALSE
)

Value

Prints correlation statistics (including CI and BF) in APA style. Furthermore, when assigned, returns a named vector with the following elements: r (Pearson correlation), p (p value), bf

(Bayes factor).

Arguments

var1

Numeric vector; numbers of the first variable.

var2

Numeric vector; numbers of the second variable.

nonparametric

Logical (FALSE by default). If TRUE, uses nonparametric tests (Spearman's rank correlation, including BFs; see Details).

ci

Numeric; confidence level for the returned CI, as implemented in cor.test.

bf_added

Logical. If TRUE (default), Bayes factor is calculated and displayed.

direction

NULL or string; optionally specifies one-sided test: either "negative" (negative correlation expected) or "positive" (positive correlation expected). (Short forms also work, e.g. "p", "pos", "neg", etc.) If NULL (default), the test is two-sided.

round_r

Number to round to the correlation and its CI.

for_table

Logical. If TRUE, omits the confidence level display from the printed text.

sb_correction

Logical. If TRUE, applies Spearman-Brown correction (2 * r / (1+r)) to the correlation (including CI).

hush

Logical. If TRUE, prevents printing any details to console.

Details

The Bayes factor (BF) is calculated with the default r-scale of 1/3 for parametric test, and with the default r-scale of 1 for nonparametric test. BF supporting null hypothesis is denoted as BF01, while that supporting alternative hypothesis is denoted as BF10. When the BF is smaller than 1 (i.e., supports null hypothesis), the reciprocal is calculated (hence, BF10 = BF, but BF01 = 1/BF). When the BF is greater than or equal to 10000, scientific (exponential) form is reported for readability. (The original full BF number is available in the returned named vector as bf.)#'

References

Brown, W. (1910). Some experimental results in the correlation of mental abilities. British Journal of Psychology, 1904-1920, 3(3), 296-322. tools:::Rd_expr_doi("https://doi.org/10.1111/j.2044-8295.1910.tb00207.x")

Eisinga, R., Grotenhuis, M. te, & Pelzer, B. (2013). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International Journal of Public Health, 58(4), 637-642. tools:::Rd_expr_doi("https://doi.org/10.1007/s00038-012-0416-3")

Spearman, C. (1910). Correlation calculated from faulty data. British Journal of Psychology, 1904-1920, 3(3), 271-295. tools:::Rd_expr_doi("https://doi.org/10.1111/j.2044-8295.1910.tb00206.x")

van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2020). Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman’s rho. Journal of Applied Statistics, 1–23. tools:::Rd_expr_doi("https://doi.org/10.1080/02664763.2019.1709053")

See Also

t_neat

Examples

# assign two variables
v1 = c(11, 15, 19, 43, 53, -4, 34, 8, 33, -1, 54 )
v2 = c(4, -2, 23, 13, 32, 16, 3, 29, 37, -4, 65 )

corr_neat(v1, v2) # prints statistics

# one-sided, and omitting the "95% CI" part
corr_neat(v1, v2, direction = 'pos', for_table = TRUE)

# print statistics and assign main results
results = corr_neat(v1, v2, direction = 'pos')

results['p'] # get precise p value