collinear_example.ml

# let pred =
    Array.init 100 (fun r ->
      let a = n1 () in
      let b = n1 () in
      let c = a +. b in (* Makes the third column/predictor redundant and our data collinear! *)
      [|a;b;c|]) ;;
val pred : float array array =
  [|[|2.1000; 0.9001; 3.0001|]; [|1.7343; 2.9934; 4.7277|]; [|2.7644; 4.0290; 6.7935|]; 
    [|0.7972; 2.6754; 3.4726|]; [|0.6089; 2.7250; 3.3340|]; [|0.7870; 2.7721; 3.5592|]; 
   ...|]
# let resp =
    Array.map (fun arr ->
      Float.( 2.0 * arr.(0) + 3.0 * arr.(1) + 4.0 * arr.(2) + n1 ())) pred ;;
val resp : float array =
  [|19.2339; 33.6270; 45.2538; 1.1948; 33.1565; ... |]
# open Regressopn ;;
# Multivariate.regress None ~pred ~resp
  |> Multivariate.coefficients ;;
- : float array = [|-80174210710646.3594; -80174210710645.5156; 80174210710652.8594|]
 (* Doesn't seem right *)
# Multivariate.regress (Some { add_constant_column = false; lambda_spec = Some (Spec 0.1)})  ~pred ~resp
  |> Multivariate.coefficients ;;
- : float array = [|1.8613; 2.7559; 4.6172|]  (* A little better *)

Regressopn