spatialEco (version 2.0-2)

partial.cor: Partial and Semi-partial correlation

Description

Calculates a partial or semi-partial correlation with parametric and nonparametric options

Usage

partial.cor(
  x,
  y,
  z,
  method = c("partial", "semipartial"),
  statistic = c("kendall", "pearson", "spearman")
)

Value

data.frame containing:

• correlation - correlation coefficient

• p.value - p-value of correlation

• test.statistic - test statistic

• n - sample size

• Method - indicating partial or semipartial correlation

• Statistic - the correlation statistic used

Arguments

x

A vector, data.frame or matrix with 3 columns

y

A vector same length as x

z

A vector same length as x

method

Type of correlation: "partial" or "semipartial"

statistic

Correlation statistic, options are: "kendall", "pearson", "spearman"

Author

Jeffrey S. Evans

Details

Partial and semipartial correlations show the association between two variables when one or more peripheral variables are controlled to hold them constant.

Suppose we have three variables, X, Y, and Z. Partial correlation holds constant one variable when computing the relations two others. Suppose we want to know the correlation between X and Y holding Z constant for both X and Y. That would be the partial correlation between X and Y controlling for Z. Semipartial correlation holds Z constant for either X or Y, but not both, so if we wanted to control X for Z, we could compute the semipartial correlation between X and Y holding Z constant for X.

Examples

air.flow = stackloss[,1]
water.temperature = stackloss[,2]
acid = stackloss[,3]

# Partial using Kendall (nonparametric) correlation
partial.cor(air.flow, water.temperature, acid)

scholar <- data.frame(
  HSGPA=c(3.0, 3.2, 2.8, 2.5, 3.2, 3.8, 3.9, 3.8, 3.5, 3.1), 
	 FGPA=c(2.8, 3.0, 2.8, 2.2, 3.3, 3.3, 3.5, 3.7, 3.4, 2.9),
  SATV =c(500, 550, 450, 400, 600, 650, 700, 550, 650, 550)) 

# Standard Pearson's correlations between HSGPA and FGPA  
cor(scholar[,1], scholar[,2])

# Partial correlation using Pearson (parametric) between HSGPA 
#   and FGPA, controlling for SATV
partial.cor(scholar, statistic="pearson")

# Semipartial using Pearson (parametric) correlation 
partial.cor(x=scholar[,2], y=scholar[,1], z=scholar[,3], 
            method="semipartial", statistic="pearson")