Partial correlation matrices are a way to examine linear relationships between two or more continuous variables while controlling for other variables
For each pair of variables, a Pearson’s r value indicates the strength and direction of the relationship between those two variables. A positive value indicates a positive relationship (higher values of one variable predict higher values of the other variable). A negative Pearson’s r indicates a negative relationship (higher values of one variable predict lower values of the other variable, and vice-versa). A value of zero indicates no relationship (whether a variable is high or low, does not tell us anything about the value of the other variable).
More formally, it is possible to test the null hypothesis that the correlation is zero and calculate a p-value. If the p-value is low, it suggests the correlation co-efficient is not zero, and there is a linear (or more complex) relationship between the two variables.
|the data as a data frame
|a vector of strings naming the variables to correlate in data
|a vector of strings naming the control variables in data
|TRUE (default) or FALSE, provide Pearson's R
|TRUE or FALSE (default), provide Spearman's rho
|TRUE or FALSE (default), provide Kendall's tau-b
|one of 'part' (default) or 'semi' specifying the type of partial correlation to calculate; partial or semipartial correlation.
|TRUE (default) or FALSE, provide significance levels
|TRUE or FALSE (default), flag significant correlations
|TRUE or FALSE (default), provide the number of cases
|one of 'corr' (default), 'pos', 'neg' specifying the alernative hypothesis; correlated, correlated positively, correlated negatively respectively.
A results object containing:
Tables can be converted to data frames with asDF or as.data.frame(). For example: