The toy model below stands in for one with a bunch more variables, transforms, lags, etc. Assume I got that stuff right.
My data is ordered in time, but is now formatted as an R time series, because I need to exclude certain periods, etc. I'd rather not make it a time series for this reason, because I think it would be easy to muck up, but if I need to, or it greatly simplifies the estimating process, I'd like to just use an integer sequence, such as index. below, to represent time if that is allowed.
My problem is a simple one (I hope). I would like to use the first part of my data to estimate the coefficients of the model. Then I want to use those estimates, and not estimates from a sliding window, to do one-ahead forecasts for each of the remaining values of that data. The idea is that the formula is applied with a sliding window even though it is not estimated with one. Obviously I could retype the model with coefficients included and then get what I want in multiple ways, with base R sapply, with tidyverse dplyr::mutate or purrr::map_dbl, etc. But I am morally certain there is some standard way of pulling the formula out of the lm object and then wielding it as one desires, that I just haven't been able to find. Example:
set.seed(1)
x1 <- 1:20
y1 <- 2 + x1 + lag(x1) + rnorm(20)
index. <- x1
data. <- tibble(index., x1, y1)
mod_eq <- y1 ~ x1 + lag(x1)
lm_obj <- lm(mod_eq, data.[1:15,])
and I want something along the lines of:
my_forecast_values <- apply_eq_to_data(eq = get_estimated_equation(lm_obj), my_data = data.[16:20])
and the lag shouldn't give me an error.
Also, this is not part of my question per se, but I could use a pointer to a nice tutorial on using R formulas and the standard estimation output objects produced by lm, glm, nls and the like. Not the statistics, just the programming.