How correlated are the variables in the data?

Create a correlation matrix of the selected variables. Correlations and p.values are provided for each variable pair. To show only those correlations above a certain (absolute) level, use the correlation cutoff box.

Note: Correlations can be calculated for variables of type `numeric`

, `integer`

, `date`

, and `factor`

. When variables of type factor are included the `Adjust for {factor} variables`

box should be checked. When correlations are estimated with adjustment, variables that are of type `factor`

will be treated as (ordinal) categorical variables and all other variables will be treated as continuous.

A visual representation of the correlation matrix is provided in the *Plot* tab. Note that scatter plots in the graph at most 1,000 data points by default. To generate scatter plots that use all observations use `plot(result, n = -1)`

in *Report > Rmd*.

Stars shown in the *Plot* tab are interpreted as:

- p.value between 0 and 0.001: ***
- p.value between 0.001 and 0.01: **
- p.value between 0.01 and 0.05: *
- p.value between 0.05 and 0.1: .

The font-size used in the plot is proportional to the size and significance of the correlation between two variables.

Select the method to use to calculate correlations. The most common method is `Pearson`

. See Wikipedia for details.

To show only correlations above a certain value choose a non-zero value in the numeric input between 0 and 1 (e.g., 0.15).

Although we generally use the correlation matrix, you can also show the covariance matrix by checking the `Show covariance matrix`

box.

The correlation matrix can be stored as a data.frame by (1) providing a name for the new data set and (2) clicking on the `Store`

button. The new data sets will the estimated `correlation`

for each variable pair and a `distance`

measure that is calculated as follows: `distance = 0.5 * (1 - correlation)`

. This measure will be equal to 1 when the correlation between two variable is equal to -1 and equal to 0 when the correlation between two variables is equal to 1. For an example of what such a dataset would look like, see the screenshot below of the *Data > View* tab. Data sets with this structure can be used as input to create a (dis)similarity based map by using *Multivariate > (Dis)similarity*.

Add code to *Report > Rmd* to (re)create the analysis by clicking the icon on the bottom left of your screen or by pressing `ALT-enter`

on your keyboard.

By default the correlation plot samples 1,000 data points. To include all data points use `plot(result, n = -1)`

To add, for example, a title to the plot use `title(main = "Correlation plot\n\n")`

. See the R graphics documentation for additional information.

For an overview of related R-functions used by Radiant to evaluate correlations see *Basics > Tables*.

The key function from the `psych`

package used in the `correlation`

tool is `corr.test`

.