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"))
Either data.frame or a matrix
The numerical variable.
What observations to use. See cor function for details.
The only option that is not available here is "pairwise.complete.obs"
.
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.
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.
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{}
.
# NOT RUN {
association(mtcars)
# }
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