I have two predictions from two different types of methods. "predictedHousePrices1" is a continuous variable and the output of a prediction from a RandomForest model, "predictedHousePrices2" is the output of the predict() function a RidgeRegression model. I'd like to compare which one better explains the variability in the real data. I'm wondering if a likelihood ratio test is the best way to do this, for example:
m1 <- lm(realHousePrices~predictedHousePrices1) # R^2 = 0.25 m2 <- lm(realHousePrices~predictedHousePrices2) # R^2 = 0.30
Is it correct to use a likelihood ratio test to check if an $R^2$ of .25 is "significantly" more than 0.30, for example:
m3 <- lm(realHousePrices~predictedHousePrices1+predictedHousePrices2) library("epicalc") lrtest(m3, m1)
Or is there a better way to do this?