Dear friends,
I need to automatically fit all possible linear regression models (with all
possible combinations of regressors), and found the MuMIn package, which
has the dredge function.
This is the dataset I am working with:
> dput(final_frame)
structure(list(y = c(41.9, 44.5, 43.9, 30.9, 27.9, 38.9, 30.9,
28.9, 25.9, 31, 29.5, 35.9, 37.5, 37.9), x1 = c(6.6969, 8.7951,
9.0384, 5.9592, 4.5429, 8.3607, 5.898, 5.6039, 4.9176, 6.2712,
5.0208, 5.8282, 5.9894, 7.5422), x4 = c(1.488, 1.82, 1.5, 1.121,
1.175, 1.777, 1.24, 1.501, 0.998, 0.975, 1.5, 1.225, 1.256, 1.69
), x8 = c(22, 50, 23, 32, 40, 48, 51, 32, 42, 30, 62, 32, 40,
22), x2 = c(1.5, 1.5, 1, 1, 1, 1.5, 1, 1, 1, 1, 1, 1, 1, 1.5),
x7 = c(3, 4, 3, 3, 3, 4, 3, 3, 4, 2, 4, 3, 3, 3)), class =
"data.frame", row.names = c(NA,
-14L))
I started with the all regressor model, which I called globalmodel as
follows:
#Fitting Regression model with all possible combinations of regressors
options(na.action = "na.fail") # change the default "na.omit" to prevent
models
globalmodel <- lm(y~., data=final_frame)
Then, the following code provides the different coefficients (for
regressors and the intercept) for each of the possible model combinations:
combinations <- dredge(globalmodel)
print(combinations)
I would like to retrieve the R-squared generated by each combination, but
have not been able to get it thus far.
Any guidance on how to retrieve the R-squared from all linear model
combinations would be greatly appreciated.
Kind regards,
Paul
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