src/onto/onto.lisp

   1
   2
   3 #+nil
   4 (progn ;; philosophy time
   5
   6   (setf my-model (model :name "ex1"
   7                         :data-slots (list w x y z)
   8                         :param-slots (list alpha beta gamma)
   9                         :math-form (regression-model :formula '(= w (+ (* beta x)
  10                                                                      (* alpha y)
  11                                                                      (* gamma z)
  12                                                                      normal-error))
  13                                                      :centrality 'median ; 'mean
  14                                                      )))
  15
  16 #| or:
  17   #R"W ~ x+  y + z "
  18 |#
  19
  20   (setf my-dataset (statistical-table :table data-frame-contents
  21                                       :metadata (list (:case-names (list ))
  22                                                       (:var-names (list ))
  23                                                       (:documentation "string of doc"))))
  24
  25   (setf my-analysis (analysis
  26                      :model my-model
  27                      :data my-dataset
  28                      :parameter-map (pairing (model-param-slots my-model)
  29                                              (data-var-names my-dataset))))
  30
  31   ;; ontological implications -- the analysis is an abstract class of
  32   ;; data, model, and mapping between the model and data.  The fit is
  33   ;; the instantiation of such.  This provides a statistical object
  34   ;; computation theory which can be realized as "executable
  35   ;; statistics" or "computable statistics".
  36   (setf my-analysis (analyze my-fit
  37                              :estimation-method 'linear-least-squares-regression))
  38
  39   ;; one of the tricks here is that one needs to provide the structure
  40   ;; from which to consider estimation, and more importantly, the
  41   ;; validity of the estimation.
  42
  43   ;;
  44   (setf linear-least-squares-regression
  45         (estimation-method-definition
  46          :variable-defintions ((list
  47                                 ;; from MachLearn: supervised,
  48                                 ;; unsupervised
  49                                 :data-response-vars list-drv ; nil if unsup
  50                                 ;;
  51                                 :param-vars list-pv
  52                                      :data-predictor-vars list-dpv
  53                                      ;; nil in this case.  these
  54                                      ;; describe "out-of-box" specs
  55                                      :hyper-vars list-hv))
  56          :form '(regression-additive-error
  57                  :central-form (linear-form drv pv dpv)
  58                  :error-form 'normal-error)
  59          :resulting-decision '(point-estimation interval-estimation)
  60          :philosophy 'frequentist
  61          :documentation "use least squares to fit a linear regression
  62                          model to data."))
  63
  64   (defparameter *statistical-philosophies*
  65     '(frequentist bayesian fiducial decision-analysis)
  66     "can be combined to build decision-making approaches and
  67     characterizations")
  68
  69   (defparameter *decisions*
  70     '(estimation selection testing)
  71     "possible results from a...")
  72   ;; is this really true?  One can embedded hypothesis testing within
  73   ;; estimation, as the hypothesis estimated to select.  And
  74   ;; categorical/continuous rear their ugly heads, but not really in
  75   ;; an essential way.
  76
  77   (defparameter *ontology-of-decision-procedures*
  78     (list :decisions
  79           (list :estimation
  80                 (list :point
  81                       (list :maximum-likelihood
  82                             :minimum-entropy
  83                             :least-squares
  84                             :method-of-moments)
  85                       :interval
  86                       (list :maximum-likelihood
  87                             :))
  88                 :testing
  89                 (list :fisherian
  90                       :neyman-pearson
  91                       (list :traditional
  92                             :bioequivalence-inversion)
  93                       :selection
  94                       (list :ranking
  95                             :top-k-of-n-select))
  96                 :parametric
  97                 :partially-parametric))
  98     "start of ontology"))