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Confidence interval for a Spearman correlation

Source: R/statpsych2.R
ci.spear.Rd

Computes a Fisher confidence interval for a population Spearman correlation. Unlike the confidence interval for a Pearson correlation, this function does not assume bivariate normality. Unlike the Pearson correlation which describes a linear bivariate relation, the Spearman correlation describes a monotonic bivariate relation. This function is not appropriate for ordered categorical variables.

For more details, see Section 1.32 of Bonett (2021, Volume 2)

Usage

ci.spear(alpha, y, x)

Arguments

alpha

alpha level for 1-alpha confidence

y

vector of y scores

x

vector of x scores (paired with y)

Value

Returns a 1-row matrix. The columns are:

  • Estimate - estimated Spearman correlation

  • SE - standard error

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

References

Bonett DG, Wright TA (2000). “Sample size requirements for estimating Pearson, Kendall and Spearman correlations.” Psychometrika, 65(1), 23–28. ISSN 0033-3123, doi:10.1007/BF02294183 .

Bonett DG (2021). Statistical Methods for Psychologists https://dgbonett.sites.ucsc.edu/.

Examples

y <- c(21, 4, 9, 12, 35, 18, 10, 22, 24, 1, 6, 8, 13, 16, 19)
x <- c(67, 28, 30, 28, 52, 40, 25, 37, 44, 10, 14, 20, 28, 40, 51)
ci.spear(.05, y, x)
#>  Estimate      SE     LL     UL
#>      0.87 0.08241 0.5841 0.9638

# Should return:
# Estimate      SE     LL     UL
#     0.87 0.08241 0.5841 0.9638