ppcor (version 1.1)

# pcor.test: Partial correlation for two variables given a third variable.

## Description

The function `pcor.test` can calculate the pairwise partial correlations between two variables. In addition, it gives us the p value as well as statistic.

## Usage

`pcor.test(x, y, z, method = c("pearson", "kendall", "spearman"))`

## Arguments

x
a numeric vector.
y
a numeric vector.
z
a numeric vector.
method
a character string indicating which partial correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman" can be abbreviated.

## Value

estimate
the partial correlation coefficient between two variables
p.value
the p value of the test
statistic
the value of the test statistic
n
the number of samples
gn
the number of given variables
method
the correlation method used

## Details

Partial correlation is the correlation of two variables while controlling for a third variable. When the determinant of variance-covariance matrix is numerically zero, Moore-Penrose generalized matrix inverse is used. In this case, no `p-value` and `statistic` will be provided if the number of variables are greater than or equal to the sample size.

## References

Kim, S. (2015) ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients. Communications for Statistical Applications and Methods, 22(6), 665-674.

`pcor`, `spcor`, `spcor.test`

## Examples

Run this code
``````# data
y.data <- data.frame(
hl=c(7,15,19,15,21,22,57,15,20,18),
disp=c(0.000,0.964,0.000,0.000,0.921,0.000,0.000,1.006,0.000,1.011),
deg=c(9,2,3,4,1,3,1,3,6,1),
BC=c(1.78e-02,1.05e-06,1.37e-05,7.18e-03,0.00e+00,0.00e+00,0.00e+00
,4.48e-03,2.10e-06,0.00e+00)
)

# partial correlation between "hl" and "disp" given "deg" and "BC"
pcor.test(y.data\$hl,y.data\$disp,y.data[,c("deg","BC")])
pcor.test(y.data[,1],y.data[,2],y.data[,c(3:4)])
pcor.test(y.data[,1],y.data[,2],y.data[,-c(1:2)])
``````

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