spcor.test: Semi-partial (part) correlation for two variables given a...

In ppcor: Partial and Semi-Partial (Part) Correlation

Description

The function spcor.test can calculate the pairwise semi-partial (part) correlations between two variables. In addition, it gives us the p value as well as statistic.

Usage

spcor.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

Semi-partial correlation is the correlation of two variables with variation from a third variable removed only from the second 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 semi-partial (part) 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

spcor, pcor, pcor.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)
			)

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

Example output

Loading required package: MASS
    estimate   p.value statistic  n gp  Method
1 -0.5791734 0.1324601 -1.740275 10  2 pearson
    estimate   p.value statistic  n gp  Method
1 -0.5791734 0.1324601 -1.740275 10  2 pearson
    estimate   p.value statistic  n gp  Method
1 -0.5791734 0.1324601 -1.740275 10  2 pearson

ppcor documentation built on May 2, 2019, 1:44 p.m.