DOC: Replace array with ndarray

Use correct type since array.py is not used or supported

statsmodels/base/distributed_estimation.py

@@ -644,8 +644,8 @@ class DistributedResults(LikelihoodModelResults):
         Class instance for model used for distributed data,
         this particular instance uses fake data and is really
         only to allow use of methods like predict.
-    params : array
-        Parameter estimates from the fit model.
+    params : ndarray
+Parameter estimates from the fit model.
     """
 
     def __init__(self, model, params):

statsmodels/base/model.py

@@ -849,13 +849,13 @@ def expandparams(self, params):
 
         Parameters
         ----------
-        params : array
-            reduced parameter array
+        params : ndarray
+reduced parameter array
 
         Returns
         -------
-        paramsfull : array
-            expanded parameter array where fixed parameters are included
+        paramsfull : ndarray
+expanded parameter array where fixed parameters are included
 
         Notes
         -----
@@ -981,8 +981,8 @@ class Results(object):
     ----------
     model : class instance
         the previously specified model instance
-    params : array
-        parameter estimates from the fit model
+    params : ndarray
+parameter estimates from the fit model
     """
     def __init__(self, model, params, **kwd):
         self.__dict__.update(kwd)
@@ -1151,8 +1151,8 @@ class LikelihoodModelResults(Results):
         The parameters estimated for the model.
     scale : float
         The scaling factor of the model given during instantiation.
-    tvalues : array
-        The t-values of the standard errors.
+    tvalues : ndarray
+The t-values of the standard errors.
 
 
     Notes
@@ -2366,10 +2366,10 @@ def bootstrap(self, nrep=100, method='nm', disp=0, store=1):
 
         Returns
         -------
-        mean : array
-            mean of parameter estimates over bootstrap replications
-        std : array
-            standard deviation of parameter estimates over bootstrap
+        mean : ndarray
+mean of parameter estimates over bootstrap replications
+        std : ndarray
+standard deviation of parameter estimates over bootstrap
             replications
 
         Notes
@@ -2442,14 +2442,14 @@ class GenericLikelihoodModelResults(LikelihoodModelResults, ResultMixin):
     bic : float
         Bayesian information criterion. -2*`llf` + ln(`nobs`)*p where p is the
         number of regressors including the intercept.
-    bse : array
-        The standard errors of the coefficients.
+    bse : ndarray
+The standard errors of the coefficients.
     df_resid : float
         See model definition.
     df_model : float
         See model definition.
-    fitted_values : array
-        Linear predictor XB.
+    fitted_values : ndarray
+Linear predictor XB.
     llf : float
         Value of the loglikelihood
     llnull : float

statsmodels/base/optimizer.py

@@ -59,8 +59,8 @@ def _fit(self, objective, gradient, start_params, fargs, kwargs,
 
         Returns
         -------
-        xopt : array
-            The solution to the objective function
+        xopt : ndarray
+The solution to the objective function
         retvals : dict, None
             If `full_output` is True then this is a dictionary which holds
             information returned from the solver used. If it is False, this is

statsmodels/discrete/_diagnostics_count.py

@@ -141,8 +141,8 @@ def test_chisquare_prob(results, probs, bin_edges=None, method=None):
     ----------
     results : results instance
         Instance of a count regression results
-    probs : array
-        Array of predicted probabilities with observations
+    probs : ndarray
+Array of predicted probabilities with observations
         in rows and event counts in columns
     bin_edges : None or array
         intervals to combine several counts into cells
@@ -212,8 +212,8 @@ def test_poisson_zeroinflation(results_poisson, exog_infl=None):
     results_poisson: results instance
         The test is only valid if the results instance is a Poisson
         model.
-    exog_infl : array
-        Explanatory variables for the zero inflated or zero modified
+    exog_infl : ndarray
+Explanatory variables for the zero inflated or zero modified
         alternative. I exog_infl is None, then the inflation
         probability is assumed to be constant.
 

statsmodels/discrete/count_model.py

@@ -43,12 +43,12 @@ class GenericZeroInflated(CountModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
-    exog_infl: array
-        A reference to the zero-inflated exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
+    exog_infl: ndarray
+A reference to the zero-inflated exogenous design.
     """ % {'params' : base._model_params_doc,
            'extra_params' : _doc_zi_params + base._missing_param_doc}
 
@@ -462,12 +462,12 @@ class ZeroInflatedPoisson(GenericZeroInflated):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
-    exog_infl: array
-        A reference to the zero-inflated exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
+    exog_infl: ndarray
+A reference to the zero-inflated exogenous design.
     """ % {'params' : base._model_params_doc,
            'extra_params' : _doc_zi_params + base._missing_param_doc}
 
@@ -550,12 +550,12 @@ class ZeroInflatedGeneralizedPoisson(GenericZeroInflated):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
-    exog_infl: array
-        A reference to the zero-inflated exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
+    exog_infl: ndarray
+A reference to the zero-inflated exogenous design.
     p: scalar
         P denotes parametrizations for ZIGP regression.
     """ % {'params' : base._model_params_doc,
@@ -628,12 +628,12 @@ class ZeroInflatedNegativeBinomialP(GenericZeroInflated):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
-    exog_infl: array
-        A reference to the zero-inflated exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
+    exog_infl: ndarray
+A reference to the zero-inflated exogenous design.
     p: scalar
         P denotes parametrizations for ZINB regression. p=1 for ZINB-1 and
     p=2 for ZINB-2. Default is p=2

statsmodels/discrete/discrete_model.py

@@ -914,10 +914,10 @@ class Poisson(CountModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
     """ % {'params' : base._model_params_doc,
            'extra_params' :
            """offset : array_like
@@ -1316,10 +1316,10 @@ class GeneralizedPoisson(CountModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
     """ % {'params' : base._model_params_doc,
            'extra_params' :
     """
@@ -1715,10 +1715,10 @@ class Logit(BinaryModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
     """ % {'params' : base._model_params_doc,
            'extra_params' : base._missing_param_doc}
 
@@ -1925,10 +1925,10 @@ class Probit(BinaryModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
     """ % {'params' : base._model_params_doc,
            'extra_params' : base._missing_param_doc}
 
@@ -2154,10 +2154,10 @@ class MNLogit(MultinomialModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
     J : float
         The number of choices for the endogenous variable. Note that this
         is zero-indexed.
@@ -2167,8 +2167,8 @@ class MNLogit(MultinomialModel):
     names : dict
         A dictionary mapping the column number in `wendog` to the variables
         in `endog`.
-    wendog : array
-        An n x j array where j is the number of unique categories in `endog`.
+    wendog : ndarray
+An n x j array where j is the number of unique categories in `endog`.
         Each column of j is a dummy variable indicating the category of
         each observation. See `names` for a dictionary mapping each column to
         its category.
@@ -2203,8 +2203,8 @@ def cdf(self, X):
 
         Parameters
         ----------
-        X : array
-            The linear predictor of the model XB.
+        X : ndarray
+The linear predictor of the model XB.
 
         Returns
         -------
@@ -2291,8 +2291,8 @@ def score(self, params):
 
         Parameters
         ----------
-        params : array
-            The parameters of the multinomial logit model.
+        params : ndarray
+The parameters of the multinomial logit model.
 
         Returns
         -------
@@ -2336,8 +2336,8 @@ def score_obs(self, params):
 
         Parameters
         ----------
-        params : array
-            The parameters of the multinomial logit model.
+        params : ndarray
+The parameters of the multinomial logit model.
 
         Returns
         -------
@@ -2478,10 +2478,10 @@ class NegativeBinomial(CountModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
 
     References
     ----------
@@ -2941,10 +2941,10 @@ class NegativeBinomialP(CountModel):
 
     Attributes
     ----------
-    endog : array
-        A reference to the endogenous response variable
-    exog : array
-        A reference to the exogenous design.
+    endog : ndarray
+A reference to the endogenous response variable
+    exog : ndarray
+A reference to the exogenous design.
     p : scalar
         P denotes parameterizations for NB-P regression. p=1 for NB-1 and
         p=2 for NB-2. Default is p=1.

statsmodels/duration/hazard_regression.py

@@ -1362,14 +1362,14 @@ class PHRegResults(base.LikelihoodModelResults):
     ----------
     model : class instance
         PHreg model instance that called fit.
-    normalized_cov_params : array
-        The sampling covariance matrix of the estimates
-    params : array
-        The coefficients of the fitted model.  Each coefficient is the
+    normalized_cov_params : ndarray
+The sampling covariance matrix of the estimates
+    params : ndarray
+The coefficients of the fitted model.  Each coefficient is the
         log hazard ratio corresponding to a 1 unit difference in a
         single covariate while holding the other covariates fixed.
-    bse : array
-        The standard errors of the fitted parameters.
+    bse : ndarray
+The standard errors of the fitted parameters.
 
     See Also
     --------