def params_transform_univariate(params, cov_params, link=None, transform=None,
row_labels=None):
"""
results for univariate, nonlinear, monotonicaly transformed parameters
This provides transformed values, standard errors and confidence interval
for transformations of parameters, for example in calculating rates with
`exp(params)` in the case of Poisson or other models with exponential
mean function.
"""
from statsmodels.genmod.families import links
if link is None and transform is None:
link = links.Log()
if row_labels is None and hasattr(params, 'index'):
row_labels = params.index
params = np.asarray(params)
predicted_mean = link.inverse(params)
link_deriv = link.inverse_deriv(params)
var_pred_mean = link_deriv**2 * np.diag(cov_params)
# TODO: do we want covariance also, or just var/se
dist = stats.norm
# TODO: need ci for linear prediction, method of `lin_pred
linpred = PredictionResults(params, np.diag(cov_params), dist=dist,
row_labels=row_labels, link=links.identity())
res = PredictionResults(predicted_mean, var_pred_mean, dist=dist,
row_labels=row_labels, linpred=linpred, link=link)
return res
def get_prediction(self, exog=None, transform=True, which="mean",
row_labels=None, average=False, agg_weights=None,
pred_kwds=None):
"""
Compute prediction results when endpoint transformation is valid.
Parameters
----------
exog : array_like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a formula, do you want to pass
exog through the formula. Default is True. E.g., if you fit
a model y ~ log(x1) + log(x2), and transform is True, then
you can pass a data structure that contains x1 and x2 in
their original form. Otherwise, you'd need to log the data
first.
which : str
Which statistic is to be predicted. Default is "mean".
The available statistics and options depend on the model.
see the model.predict docstring
linear : bool
Linear has been replaced by the `which` keyword and will be
deprecated.
If linear is True, then `which` is ignored and the linear
prediction is returned.
row_labels : list of str or None
If row_lables are provided, then they will replace the generated
labels.
average : bool
If average is True, then the mean prediction is computed, that is,
predictions are computed for individual exog and then the average
over observation is used.
If average is False, then the results are the predictions for all
observations, i.e. same length as ``exog``.
agg_weights : ndarray, optional
Aggregation weights, only used if average is True.
The weights are not normalized.
**kwargs :
Some models can take additional keyword arguments, such as offset,
exposure or additional exog in multi-part models like zero inflated
models.
See the predict method of the model for the details.
Returns
-------
prediction_results : PredictionResults
The prediction results instance contains prediction and prediction
variance and can on demand calculate confidence intervals and
summary dataframe for the prediction.
Notes
-----
Status: new in 0.14, experimental
"""
use_endpoint = getattr(self.model, "_use_endpoint", True)
if which == "linear":
res = get_prediction_linear(
self,
exog=exog,
transform=transform,
row_labels=row_labels,
pred_kwds=pred_kwds,
)
elif (which == "mean")and (use_endpoint is True) and (average is False):
# endpoint transformation
k1 = self.model.exog.shape[1]
if len(self.params > k1):
# TODO: we allow endpoint transformation only for the first link
index = np.arange(k1)
else:
index = None
pred_kwds["which"] = which
# TODO: add link or ilink to all link based models (except zi
link = getattr(self.model, "link", None)
if link is None:
from statsmodels.genmod.families import links
link = links.Log()
res = get_prediction_monotonic(
self,
exog=exog,
transform=transform,
row_labels=row_labels,
link=link,
pred_kwds=pred_kwds,
index=index,
)
else:
# which is not mean or linear, or we need averaging
res = get_prediction_delta(
self,
exog=exog,
which=which,
average=average,
agg_weights=agg_weights,
pred_kwds=pred_kwds,
)
return res