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:

$CI_w = 0.45304^r \times 2.25152 \times n^{-0.50089}$

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. doi:10.2134/agronj2016.04.0196

## Author

Tiago Olivoto tiagoolivoto@gmail.com

## Examples

# \donttest{
library(metan)

CI1 <- corr_ci(data_ge2)
#> # A tibble: 105 × 7
#>    V1    V2     Corr     n     CI    LL    UL
#>    <chr> <chr> <dbl> <int>  <dbl> <dbl> <dbl>
#>  1 PH    EH    0.932   156 0.0858 0.846 1.02
#>  2 PH    EP    0.638   156 0.108  0.530 0.747
#>  3 PH    EL    0.380   156 0.133  0.247 0.513
#>  4 PH    ED    0.661   156 0.106  0.555 0.768
#>  5 PH    CL    0.325   156 0.139  0.186 0.464
#>  6 PH    CD    0.315   156 0.140  0.176 0.455
#>  7 PH    CW    0.505   156 0.120  0.384 0.625
#>  8 PH    KW    0.753   156 0.0988 0.655 0.852
#>  9 PH    NR    0.329   156 0.138  0.190 0.467
#> 10 PH    NKR   0.353   156 0.136  0.217 0.489
#> # … with 95 more rows

# By each level of the factor 'ENV'
CI2 <- corr_ci(data_ge2, CD, TKW, NKE,
by = ENV,
verbose = FALSE)
CI2
#> # A tibble: 12 × 8
#>    ENV   V1    V2       Corr     n    CI      LL      UL
#>    <fct> <chr> <chr>   <dbl> <int> <dbl>   <dbl>   <dbl>
#>  1 A1    CD    TKW    0.385     39 0.265  0.120   0.650
#>  2 A1    CD    NKE   -0.0205    39 0.354 -0.374   0.333
#>  3 A1    TKW   NKE   -0.589     39 0.225 -0.814  -0.363
#>  4 A2    CD    TKW    0.518     39 0.238  0.280   0.756
#>  5 A2    CD    NKE    0.710     39 0.205  0.505   0.915
#>  6 A2    TKW   NKE    0.0755    39 0.338 -0.263   0.414
#>  7 A3    CD    TKW    0.270     39 0.290 -0.0200  0.560
#>  8 A3    CD    NKE    0.271     39 0.290 -0.0194  0.561
#>  9 A3    TKW   NKE   -0.389     39 0.264 -0.653  -0.125
#> 10 A4    CD    TKW    0.417     39 0.258  0.158   0.675
#> 11 A4    CD    NKE    0.477     39 0.246  0.230   0.723
#> 12 A4    TKW   NKE   -0.259     39 0.293 -0.552   0.0334
# }