# Automated predictive model fitting with variables chosen based on accessory data frame

The Setup: I am performing an exhaustive search of multiple linear regression models with the R package leaps. The package does return vectors of certain fit statistics (i.e. BIC and r-squared). However, there are a few other fit criteria (i.e. CCC) I would like to generate for a subset of the models (~100). Instead of fitting the subset of models manually, I want to leverage a data.frame provided by leaps for automated refitting of predictive multiple linear models.

The Challenge: How can I code a FOR loop (or some other function) to refit linear models? I have two data.frames to use: 1) the data (response + all explanatory variables) and 2) a data.frame describing what variables where in each model. The second data.frame has boolean (TRUE/FALSE) indicators of whether each explanatory variable (columns) was incorporated in the linear model (rows). Below is a representation of that data.frame.

     (Intercept)    var1  var2 var3
X5          TRUE    TRUE FALSE FALSE
X6          TRUE    TRUE FALSE FALSE
X7.2        TRUE    TRUE FALSE FALSE
X7.4        TRUE    TRUE FALSE FALSE
X8.2        TRUE    TRUE FALSE  TRUE


My major hang up is I don't know how to automate generating a formula that works for the lm function.