metan (version 1.15.0)

corr_ci: Confidence interval for correlation coefficient

Description

Computes the half-width confidence interval for correlation coefficient using the nonparametric method proposed by Olivoto et al. (2018).

The half-width confidence interval is computed according to the following equation:

CI_w = 0.45304^r 2.25152 n^-0.50089

where n is the sample size and r is the correlation coefficient.

Usage

corr_ci(
  .data = NA,
  ...,
  r = NULL,
  n = NULL,
  by = NULL,
  sel.var = NULL,
  verbose = TRUE
)

Arguments

.data

The data to be analyzed. It can be a data frame (possible with grouped data passed from dplyr::group_by()) or a symmetric correlation matrix.

...

Variables to compute the confidence interval. If not informed, all the numeric variables from .data are used.

r

If data is not available, provide the value for correlation coefficient.

n

The sample size if data is a correlation matrix or if r is informed.

by

One variable (factor) to compute the function by. It is a shortcut to dplyr::group_by(). To compute the statistics by more than one grouping variable use that function.

sel.var

A variable to shows the correlation with. This will omit all the pairwise correlations that doesn't contain sel.var.

verbose

If verbose = TRUE then some results are shown in the console.

Value

A tibble containing the values of the correlation, confidence interval, upper and lower limits for all combination of variables.

References

Olivoto, T., A.D.C. Lucio, V.Q. Souza, M. Nardino, M.I. Diel, B.G. Sari, D.. K. Krysczun, D. Meira, and C. Meier. 2018. Confidence interval width for Pearson's correlation coefficient: a Gaussian-independent estimator based on sample size and strength of association. Agron. J. 110:1-8. 10.2134/agronj2016.04.0196

Examples

# NOT RUN {
library(metan)

CI1 <- corr_ci(data_ge2)

# By each level of the factor 'ENV'
CI2 <- corr_ci(data_ge2, CD, TKW, NKE,
               by = ENV,
               verbose = FALSE)
CI2
# }
# NOT RUN {
# }