ppcor-package: Partial and Semi-partial (Part) Correlation

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

Description

Calculates parital and semi-partial (part) correlations along with p value.

Details

Package:	ppcor
Type:	Package
Version:	1.0
Date:	2011-06-14
License:	GPL-2

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.

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
pcor(y.data) 

# 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)])

# semi-partial (part) correlation
spcor(y.data) 

# 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
             hl       disp        deg        BC
hl    1.0000000 -0.6720863 -0.6161163 0.1148459
disp -0.6720863  1.0000000 -0.7215522 0.2855420
deg  -0.6161163 -0.7215522  1.0000000 0.6940953
BC    0.1148459  0.2855420  0.6940953 1.0000000

$p.value
             hl       disp        deg         BC
hl   0.00000000 0.06789202 0.10383620 0.78654997
disp 0.06789202 0.00000000 0.04332869 0.49299871
deg  0.10383620 0.04332869 0.00000000 0.05615021
BC   0.78654997 0.49299871 0.05615021 0.00000000

$statistic
             hl       disp       deg        BC
hl    0.0000000 -2.2232666 -1.916030 0.2831875
disp -2.2232666  0.0000000 -2.552768 0.7298173
deg  -1.9160295 -2.5527682  0.000000 2.3617433
BC    0.2831875  0.7298173  2.361743 0.0000000

$n
[1] 10

$gp
[1] 2

$method
[1] "pearson"

    estimate    p.value statistic  n gp  Method
1 -0.6720863 0.06789202 -2.223267 10  2 pearson
    estimate    p.value statistic  n gp  Method
1 -0.6720863 0.06789202 -2.223267 10  2 pearson
    estimate    p.value statistic  n gp  Method
1 -0.6720863 0.06789202 -2.223267 10  2 pearson
$estimate
             hl       disp        deg         BC
hl    1.0000000 -0.5791734 -0.4991364 0.07377194
disp -0.5505041  1.0000000 -0.6320921 0.18071040
deg  -0.3180603 -0.4237587  1.0000000 0.39204867
BC    0.0669124  0.1724434  0.5580398 1.00000000

$p.value
            hl      disp       deg        BC
hl   0.0000000 0.1324601 0.2079431 0.8621787
disp 0.1573861 0.0000000 0.0926718 0.6684724
deg  0.4426360 0.2954469 0.0000000 0.3367589
BC   0.8749132 0.6830213 0.1506047 0.0000000

$statistic
             hl       disp       deg        BC
hl    0.0000000 -1.7402746 -1.410960 0.1811974
disp -1.6152392  0.0000000 -1.998086 0.4500579
deg  -0.8217590 -1.1459717  0.000000 1.0438882
BC    0.1642694  0.4288224  1.647252 0.0000000

$n
[1] 10

$gp
[1] 2

$method
[1] "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
    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.