Is there a way to use cor function with factor variables without creating dummy variables? (R)

Question

I have a dataset with several categorical predictors with varying factor levels. Is there a way to generate a correlation matrix from this data without having to create a bunch of dummy variables?

I'm using multiple linear regression to predict a continuous variable (sales). The predicted values are surprisingly accurate and plotting the predicted vs observed results in a near diagonal line.

I thought that was all I needed to worry about, but in researching, I found I should also plot predicted vs residuals to test for homoscedasticity. I did that and found out I was violating it.

I was looking for a way to resolve this and found a post that said I should use a robust method for computing the covariance matrix. Hence why I want to use the cor() function, though I’m not sure if that’s actually the right way of going about this.

And here are the actual graphs:

Predicted vs Actual...

Predicted vs Residual...

Fabian August · Answer

You're going to want to use the lmtest package for re-estimating the model along with the sandwich package for the robust covariance matrix

fit <- lm(sales ~ race + age + ...)

install.packages(sandwich)
install.packages(lmtest)
library(sandwich)
library(lmtest)

coeftest(fit, vcov = vcovHC(fit, type="HC"))

Type "HC" is the original White's estimator, the default in vcovHC is "HC3" and the reason for this is given in the documentation ?vcovHC

Is there a way to use cor function with factor variables without creating dummy variables? (R)

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