I have fitted a multivariate polynomial using the lm() and step() functions in R. My data has dependent variable Y and some independent variables X1 till Xn. I formatted the formula to fit as follows: Y ~ I(X1^1)+I(X1^2)+I(X2^1)+... etc. When I use the predict() function on the original data everything works, even on the validation points which weren't used for the fit. But, I have to use the predict() function on some simulated data I produced. I made sure the simulated data is in a data.frame and all the elements are of type double like the original data. I copied the column names from the original data (X1, ... ,Xn) to the simulated data. Now when I use the predict() function I get the following error:
Error: variables ‘I(X1^1)’, ‘I(X1^2)’, ‘I(X2^1)’ were specified with different types from the fit
I really don't get it. The column names are the same, the types are the same and both original and simulated data are in a data.frame. What is happening here?
Thanks in advance!!
Sorry for not providing a reproducible example. But I've found a solution. It's not very elegant but here it is. When I coerce the data.frame with the original data to a matrix and then straight back to a data frame again some attributes and other stuff are cut off the original data. If I now use this data.frame for the fitting process the predict() function works also on the simulated data. The simulated data was in matrix format first and was converted to a data.frame. It's still not clear to me if there isn't a more elegant way to get rid off the attr, dimensions and other stuff in the data.frame of the original data. I've tried unname() but that didn't do the job.