I'm constructing a linear model from a data set with 10 variables and my current "best" model uses 4 variables. I've tested the variables and not all of them show significance, so the most that I might add to the model might be 5 variables overall. However, with 4 and 5 variables, eventhough I get very good
Pr(>|t|) values for all the variables that I've added and while my $R^2$ value has been improving, it's still just "only" $0.4287$ and the scale is $[0,1]$.
Should I be happy with $< 0.5$ $R^2$ value of should I aim to improve it in order to improve my model even more? What can I do to increase the $R^2$ value now that I have already added all variables that I can add as predictors? Should I look into interaction terms? Or something else?