leaspy.models.joint
===================

.. py:module:: leaspy.models.joint


Classes
-------

.. autoapisummary::

   leaspy.models.joint.JointModel


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

.. py:class:: JointModel(name, **kwargs)

   Bases: :py:obj:`leaspy.models.logistic.LogisticModel`


   
   Joint model for multiple repeated measures (logistic) and multiple competing events.
   The model implemented is associated to this [publication](https://arxiv.org/abs/2501.08960) on arxiv.


   :Parameters:

       **name** : :obj:`str`
           The name of the model.

       **\*\*kwargs**
           Hyperparameters of the model (including `noise_model`)







   :Raises:

       :exc:`.LeaspyModelInputError`
           * If `name` is not one of allowed sub-type: 'univariate_linear' or 'univariate_logistic'
           * If hyperparameters are inconsistent







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   .. py:attribute:: init_tolerance
      :type:  float
      :value: 0.3



   .. py:method:: get_variables_specs()

      
      Return the specifications of the variables (latent variables, derived variables,
      model 'parameters') that are part of the model.





      :Returns:

          NamedVariables
              The specifications of the model's variables.











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   .. py:method:: to_dict(*, with_mixing_matrix = True)

      
      Export model as a dictionary ready for export. Add the number of events compare to the multivariate output


      :Parameters:

          **with_mixing_matrix** : :obj:`bool`
              If True the mixing matrix will be saved in the dictionary



      :Returns:

          KwargsType
              The model instance serialized as a dictionary.











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   .. py:method:: put_individual_parameters(state, dataset)

      
      Initialise the individual parameters of the state thanks to the dataset.


      :Parameters:

          **state** : :class:`State`
              where all the variables of the model are stored

          **dataset** : :class:`Dataset`
              Where the individual data are stored



      :Returns:

          None
              ..











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   .. py:method:: compute_individual_trajectory(timepoints, individual_parameters, *, skip_ips_checks = False)

      
      This method computes the individual trajectory of a patient for given timepoint(s) using his/her individual parameters (random effects).
      For the longitudinal sub-model:
          - Compute longitudinal values
      For the event sub-model:
          - only one event: return the survival rate corrected by the probability of the first time point of the prediction assuming that the patient was alive,
          - more than one event: return the Cumulative Incidence function corrected by the probability of the first time point of the prediction assuming that the patient was alive.
      Nota: model uses its current internal state.


      :Parameters:

          **timepoints** : scalar or array_like[scalar] (:obj:`list`, :obj:`tuple`, :class:`numpy.ndarray`)
              Contains the age(s) of the subject.

          **individual_parameters** : :obj:`dict`
              Contains the individual parameters.
              Each individual parameter should be a scalar or array_like.

          **skip_ips_checks** : :obj:`bool` (default: ``False``)
              Flag to skip consistency/compatibility checks and tensorization
              of ``individual_parameters`` when it was done earlier (speed-up).



      :Returns:

          :class:`torch.Tensor`
              Contains the subject's scores computed at the given age(s)
              Shape of tensor is ``(1, n_tpts, n_features)``.




      :Raises:

          :exc:`.LeaspyModelInputError`
              If computation is tried on more than 1 individual.

          :exc:`.LeaspyIndividualParamsInputError`
              if invalid individual parameters.







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