leaspy.models.lme
=================

.. py:module:: leaspy.models.lme


Classes
-------

.. autoapisummary::

   leaspy.models.lme.LMEModel


Module Contents
---------------

.. py:class:: LMEModel(name, with_random_slope_age = True, **kwargs)

   Bases: :py:obj:`leaspy.models.stateless.StatelessModel`


   
   LMEModel is a benchmark model that fits and personalize a linear mixed-effects model.

   The model specification is the following:

   .. math:: y_{ij} = fixed_{intercept} + random_{intercept_i} + (fixed_{slopeAge} + random_{slopeAge_i}) * age_{ij} + \epsilon_{ij}

   with:
       * :math:`y_{ij}`: value of the feature of the i-th subject at his j-th visit,
       * :math:`age_{ij}`: age of the i-th subject at his j-th visit.
       * :math:`\epsilon_{ij}`: residual Gaussian noise (independent between visits)

   .. warning::
       This model must be fitted on one feature only (univariate model).

   TODO? add some covariates in this very simple model.

   :Parameters:

       **name** : :obj:`str`
           The model's name.

       **\*\*kwargs**
           Model hyperparameters:
               * with_random_slope_age : :obj:`bool` (default ``True``).

   :Attributes:

       **name** : :obj:`str`
           The model's name.

       **is_initialized** : :obj:`bool`
           ``True`` if the model is initialized, ``False`` otherwise.

       **with_random_slope_age** : :obj:`bool` (default ``True``)
           Has the LME a random slope for subject's age?
           Otherwise it only has a random intercept per subject.

       **features** : :obj:`list` of :obj:`str`
           List of the model features.
           
           .. warning::
               LME has only one feature.

       **dimension** : :obj:`int`
           Will always be 1 (univariate).

       **parameters** : :obj:`dict`
           Contains the model parameters. In particular:
               * ``ages_mean`` : :obj:`float`
                   Mean of ages (for normalization).
               * ``ages_std`` : :obj:`float`
                   Std-dev of ages (for normalization).
               * ``fe_params`` : :class:`np.ndarray` of :obj:`float`
                   Fixed effects.
               * ``cov_re`` : :class:`np.ndarray`
                   Variance-covariance matrix of random-effects.
               * ``cov_re_unscaled_inv`` : :class:`np.ndarray`
                   Inverse of unscaled (= divided by variance of noise) variance-covariance matrix of random-effects.
                   This matrix is used for personalization to new subjects.
               * ``noise_std`` : :obj:`float`
                   Std-dev of Gaussian noise.
               * ``bse_fe``, ``bse_re`` : :class:`np.ndarray` of :obj:`float`
                   Standard errors on fixed-effects and random-effects respectively (not used in ``Leaspy``).









   .. seealso::

       
       :class:`~leaspy.algo.others.lme_fit.LMEFitAlgorithm`
           ..
       :class:`~leaspy.algo.others.lme_personalize.LMEPersonalizeAlgorithm`
           ..
       



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   .. py:attribute:: with_random_slope_age
      :value: True



   .. py:attribute:: dimension
      :value: 1


      
      Number of features.





      :Returns:

          :obj:`int`, optional
              The dimension of the model, or None if not initialized.











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   .. py:property:: hyperparameters
      :type: leaspy.utils.typing.DictParamsTorch


      
      Dictionary of values for model hyperparameters.
















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   .. py:method:: compute_individual_trajectory(timepoints, individual_parameters)

      
      Compute scores values at the given time-point(s) given a subject's individual parameters.


      :Parameters:

          **timepoints** : array-like of ages (not normalized)
              Timepoints to compute individual trajectory at.

          **individual_parameters** : :obj:`dict`
              Individual parameters:
                  * random_intercept
                  * random_slope_age (if ``with_random_slope_age == True``)



      :Returns:

          :class:`torch.Tensor` of :obj:`float`
              The individual trajectories. The shape of the tensor is
              ``(n_individuals == 1, n_tpts == len(timepoints), n_features == 1)``.











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