pcor.test {ppcor}

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.

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.

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

Note

Missing values are not allowed

Author(s)

Seongho Kim <[e-mail]>

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.

See Also

pcor, spcor, spcor.test

Examples

# 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)])