Question

I was trying to automate a final step model building. I would like to combine predictors from two separate models into one final model. I played around with update.formula() but realized I can update an old lmfit$call to a new one, e.g update.formula(lmfit$call,lmfitnew$call). here i need to cherry pick variables from both models and run the final one

lmfit1 <- lm(y~ x1+x2+x3, data = modelready)
best.ngc_fit <- stepAIC(lmfit1, direction="backward")
best.ngc_fit$call

lm(formula = y~ x2+x3, data = modelready)

lmfit2 <- lm(y ~ a+b+c+d+f, data=fcstmodel)
best.fcst_fit <- stepAIC(lmfit2, direction ="backward")
best.fcst_fit$call

lm(formula = y~ a+c+d+f, data = fcstmodel)

This is what I would like to have in my final model

best.full_fit <- lm(y~x2+x3+a+c+d+f, data = fullmodel) 

I can do it manually without a problem, but I would like to automate it in order to make the whole process less tedious.

Any help will be much appreciated

Answer 1

For more advanced manipulation of fomulas you can use Formula package.

formula(as.Formula(terms(lm1),formula(Formula(terms(lm2)), lhs=0)), collapse=TRUE)

y ~ X1 + X2 + X3 + (X5 + X7)