Function returns the matrix of measures of association for different types of variables.
association(x, y =NULL, use = c("na.or.complete","complete.obs","everything","all.obs"), method = c("auto","pearson","spearman","kendall","cramer"))assoc(x, y =NULL, use = c("na.or.complete","complete.obs","everything","all.obs"), method = c("auto","pearson","spearman","kendall","cramer"))
Arguments
x: Either data.frame or a matrix
y: The numerical variable.
use: What observations to use. See cor function for details. The only option that is not available here is "pairwise.complete.obs".
method: Which method to use for the calculation of measures of association. By default this is "auto", which means that the function will use: cor , mcor or cramer - depending on the scales of variables. The other options force the function to use one and the same method for all the variables:
"pearson" - Pearson's correlation coefficient using cor ;
"spearman" - Spearman's correlation coefficient based on cor ;
"kendall" - Kendall's correlation coefficient via cor ;
"cramer" - Cramer's V using cramer ;
Be aware that the wrong usage of measures of association might give misleading results.
Returns
The following list of values is returned:
value - Matrix of the coefficients of association;
p.value - The p-values for the parameters;
type - The matrix of the types of measures of association.
Details
The function looks at the types of the variables and calculates different measures depending on the result:
If both variables are numeric, then Pearson's correlation is calculated;
If both variables are categorical, then Cramer's V is calculated;
Finally, if one of the variables is categorical, and the other is numeric, then multiple correlation is returned.
After that the measures are wrapped up in a matrix.
Function also calculates the p-values associated with the respective measures (see the return).
See details in the vignette "Marketing analytics with greybox": vignette("maUsingGreybox","greybox")
assoc() is just a short name for the association{}.