# [R] Obtaining R-squared from All Possible Combinations of Linear Models Fitted

Jan van der Laan rhe|p @end|ng |rom eoo@@dd@@n|
Tue Jul 18 13:49:15 CEST 2023

```The dredge function has a `extra` argument to get other statistics:

optional additional statistics to be included in the result, provided as
functions, function names or a list of such (preferably named or
quoted). As with the rank argument, each function must accept as an
argument a fitted model object and return (a value coercible to) a
numeric vector. This could be, for instance, additional information
criteria or goodness-of-fit statistics. The character strings "R^2" and
"adjR^2" are treated in a special way and add a likelihood-ratio based
R² and modified-R² to the result, respectively (this is more efficient
than using r.squaredLR directly).

HTH
Jan

On 17-07-2023 19:24, Paul Bernal wrote:
> 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
>
> 	[[alternative HTML version deleted]]
>
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