corr_ci: Confidence interval for correlation coefficient
In metan: Multi Environment Trials Analysis

corr_ci

R Documentation

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: \loadmathjax

\mjsdeqn

CI_w = 0.45304^r \times 2.25152 \times n^-0.50089

where \mjseqnn is the sample size and \mjseqnr 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.

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com

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. doi: 10.2134/agronj2016.04.0196

Examples


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

metan documentation built on March 7, 2023, 5:34 p.m.