mcor | R Documentation |
Function calculates multiple correlation between y and x, constructing a linear regression model
mcor(x, y, use = c("na.or.complete", "complete.obs", "everything",
"all.obs"))
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 |
This is based on the linear regression model with the set of variables in x. The returned value is just a coefficient of multiple correlation from regression, the F-statistics of the model (thus testing the null hypothesis that all the parameters are equal to zero), the associated p-value and the degrees of freedom.
See details in the vignette "Marketing analytics with greybox":
vignette("maUsingGreybox","greybox")
The following list of values is returned:
value - The value of the coefficient;
statistic - The value of F-statistics associated with the parameter;
p.value - The p-value of F-statistics associated with the parameter;
df.residual - The number of degrees of freedom for the residuals;
df - The number of degrees of freedom for the data.
Ivan Svetunkov, ivan@svetunkov.com
table, tableplot, spread,
cramer, association
mcor(mtcars$am, mtcars$mpg)
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