## Correlation Matrix

Correlation matrices are a way to examine linear relationships between two or more continuous 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.

### Arguments

 data the data as a data frame vars a vector of strings naming the variables to correlate in data pearson TRUE (default) or FALSE, provide Pearson's R spearman TRUE or FALSE (default), provide Spearman's rho kendall TRUE or FALSE (default), provide Kendall's tau-b sig TRUE (default) or FALSE, provide significance levels flag TRUE or FALSE (default), flag significant correlations n TRUE or FALSE (default), provide the number of cases ci TRUE or FALSE (default), provide confidence intervals ciWidth a number between 50 and 99.9 (default: 95), the width of confidence intervals to provide plots TRUE or FALSE (default), provide a correlation matrix plot plotDens TRUE or FALSE (default), provide densities in the correlation matrix plot plotStats TRUE or FALSE (default), provide statistics in the correlation matrix plot hypothesis one of 'corr' (default), 'pos', 'neg' specifying the alernative hypothesis; correlated, correlated positively, correlated negatively respectively.

### Returns

A results object containing:

 results\$matrix a table results\$plot an image

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results\$matrix\$asDF

as.data.frame(results\$matrix)