# partial.cor

##### Partial and Semi-partial correlation

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

##### Usage

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

##### 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"

##### 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.

##### 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

##### Examples

```
# NOT RUN {
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")
# }
```

*Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3*