fitting and predicting models using just coefficients

fit_predict_lm.R

fit_square_root = function(x, y){
  sr_x = sqrt(x)
  # assumes you have an intercept, so need to add the ones to the matrix
  X = matrix(c(rep(1, length(x)), sr_x), nrow = length(x), ncol = 2, byrow = FALSE)

  sr_fit = stats::lm.fit(X, y)
  names(sr_fit$coefficients) = NULL

  sr_fit$coefficients
}

predict_sr = function(x, coefficients){
  sr_x = sqrt(x)
  # assumes there is an intercept term
  X = matrix(c(rep(1, length(x)), sr_x), nrow = length(x), ncol = 2, byrow = FALSE)
  coef_matrix = matrix(coefficients, nrow = 2, ncol = 1, byrow = TRUE)
  out_frequency = X %*% coef_matrix
  out_frequency[, 1]
}