Runs multiple Fisher scoring steps

Runs multiple Fisher scoring steps

# S3 method for tensorflow.tensor
glm_fit(
  x,
  response,
  model,
  model_coefficients_start = NULL,
  predicted_linear_response_start = NULL,
  l2_regularizer = NULL,
  dispersion = NULL,
  offset = NULL,
  convergence_criteria_fn = NULL,
  learning_rate = NULL,
  fast_unsafe_numerics = TRUE,
  maximum_iterations = NULL,
  name = NULL,
  ...
)

Arguments

x	float-like, matrix-shaped Tensor where each row represents a sample's features.
response	vector-shaped Tensor where each element represents a sample's observed response (to the corresponding row of features). Must have same dtype as x.
model	a string naming the model (see glm_families) or a tfp$glm$ExponentialFamily-like instance which implicitly characterizes a negative log-likelihood loss by specifying the distribuion's mean, gradient_mean, and variance.
model_coefficients_start	Optional (batch of) vector-shaped Tensor representing the initial model coefficients, one for each column in x. Must have same dtype as model_matrix. Default value: Zeros.
predicted_linear_response_start	Optional Tensor with shape, dtype matching response; represents offset shifted initial linear predictions based on model_coefficients_start. Default value: offset if model_coefficients is NULL, and tf$linalg$matvec(x, model_coefficients_start) + offset otherwise.
l2_regularizer	Optional scalar Tensor representing L2 regularization penalty. Default: NULL ie. no regularization.
dispersion	Optional (batch of) Tensor representing response dispersion.
offset	Optional Tensor representing constant shift applied to predicted_linear_response.
convergence_criteria_fn	callable taking: is_converged_previous, iter_, model_coefficients_previous, predicted_linear_response_previous, model_coefficients_next, predicted_linear_response_next, response, model, dispersion and returning a logical Tensor indicating that Fisher scoring has converged.
learning_rate	Optional (batch of) scalar Tensor used to dampen iterative progress. Typically only needed if optimization diverges, should be no larger than 1 and typically very close to 1. Default value: NULL (i.e., 1).
fast_unsafe_numerics	Optional Python bool indicating if faster, less numerically accurate methods can be employed for computing the weighted least-squares solution. Default value: TRUE (i.e., "fast but possibly diminished accuracy").
maximum_iterations	Optional maximum number of iterations of Fisher scoring to run; "and-ed" with result of convergence_criteria_fn. Default value: NULL (i.e., infinity).
name	usesed as name prefix to ops created by this function. Default value: "fit".
...	other arguments passed to specific methods.

Value

A glm_fit object with parameter estimates, and number of required steps.

See also

Other glm_fit: glm_families, glm_fit_one_step.tensorflow.tensor()