def summary(self, yname=None, xname=None, title=None, alpha=0.05):
df = pd.DataFrame()
df["Type"] = (["Mean"] * self.k_exog + ["Scale"] * self.k_scale +
["Smooth"] * self.k_smooth + ["SD"] * self.k_noise)
df["coef"] = self.params
try:
df["std err"] = np.sqrt(np.diag(self.cov_params()))
except Exception:
df["std err"] = np.nan
from scipy.stats.distributions import norm
df["tvalues"] = df.coef / df["std err"]
df["P>|t|"] = 2 * norm.sf(np.abs(df.tvalues))
f = norm.ppf(1 - alpha / 2)
df["[%.3f" % (alpha / 2)] = df.coef - f * df["std err"]
df["%.3f]" % (1 - alpha / 2)] = df.coef + f * df["std err"]
df.index = self.model.data.param_names
summ = summary2.Summary()
if title is None:
title = "Gaussian process regression results"
summ.add_title(title)
summ.add_df(df)
return summ
def summary(self):
df = pd.DataFrame()
m = self.model.k_fep + self.model.k_vcp
df["Type"] = (["F" for k in range(self.model.k_fep)] +
["R" for k in range(self.model.k_vcp)])
df["Post. Mean"] = self.params[0:m]
if self.cov_params.ndim == 2:
v = np.diag(self.cov_params)[0:m]
df["Post. SD"] = np.sqrt(v)
else:
df["Post. SD"] = np.sqrt(self.cov_params[0:m])
# Convert variance parameters to natural scale
df["VC"] = np.exp(df["Post. Mean"])
df["VC (LB)"] = np.exp(df["Post. Mean"] - 2 * df["Post. SD"])
df["VC (UB)"] = np.exp(df["Post. Mean"] + 2 * df["Post. SD"])
df["VC"] = ["%.3f" % x for x in df.VC]
df["VC (LB)"] = ["%.3f" % x for x in df["VC (LB)"]]
df["VC (UB)"] = ["%.3f" % x for x in df["VC (UB)"]]
df.loc[df.index < self.model.k_fep, "VC"] = ""
df.loc[df.index < self.model.k_fep, "VC (LB)"] = ""
df.loc[df.index < self.model.k_fep, "VC (UB)"] = ""
df.index = self.model.fep_names + self.model.vcp_names
summ = summary2.Summary()
summ.add_title(self.model.family.__class__.__name__ +
" Mixed GLM Results")
summ.add_df(df)
return summ
def summary(self):
summ = summary2.Summary()
summ.add_title('Factor analysis results')
loadings_no_rot = pd.DataFrame(
self.loadings_no_rot,
columns=["factor %d" % (i)
for i in range(self.loadings_no_rot.shape[1])],
index=self.endog_names
)
if hasattr(self, "eigenvals"):
# eigenvals not available for ML method
eigenvals = pd.DataFrame(
[self.eigenvals], columns=self.endog_names, index=[''])
summ.add_dict({'': 'Eigenvalues'})
summ.add_df(eigenvals)
communality = pd.DataFrame([self.communality],
columns=self.endog_names, index=[''])
summ.add_dict({'': ''})
summ.add_dict({'': 'Communality'})
summ.add_df(communality)
summ.add_dict({'': ''})
summ.add_dict({'': 'Pre-rotated loadings'})
summ.add_df(loadings_no_rot)
summ.add_dict({'': ''})
if self.rotation_method is not None:
loadings = pd.DataFrame(
self.loadings,
columns=["factor %d" % (i)
for i in range(self.loadings.shape[1])],
index=self.endog_names
)
summ.add_dict({'': '%s rotated loadings' % (self.rotation_method)})
summ.add_df(loadings)
return summ
def summary2(self, xname=None, yname=None, title=None, alpha=.05,
float_format="%.4f"):
"""Experimental summary function for regression results
Parameters
----------
xname : List of strings of length equal to the number of parameters
Names of the independent variables (optional)
yname : string
Name of the dependent variable (optional)
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
float_format: string
print format for floats in parameters summary
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary2.Summary : class to hold summary results
"""
from statsmodels.iolib import summary2
smry = summary2.Summary()
smry.add_base(results=self, alpha=alpha, float_format=float_format,
xname=xname, yname=yname, title=title)
return smry
def summary(self):
summ = summary2.Summary()
summ.add_title("Regression FDR results")
summ.add_df(self.fdr_df)
return summ
def summary(self):
summ = summary2.Summary()
summ.add_title('Cancorr results')
summ.add_df(self.stats)
summ.add_dict({'': ''})
summ.add_dict({'Multivariate Statistics and F Approximations': ''})
summ.add_df(self.stats_mv)
return summ
def summary(self):
"""create summary results
Returns
-------
summary : summary2.Summary instance
"""
summ = summary2.Summary()
summ.add_title('Anova')
summ.add_df(self.anova_table)
return summ
def summary2(self,
xname=None,
yname=None,
title=None,
alpha=.05,
float_format="%.4f"):
"""Experimental summary function for regression results
Parameters
----------
yname : str
Name of the dependent variable (optional)
xname : list[str], optional
Names for the exogenous variables. Default is `var_##` for ## in
the number of regressors. Must match the number of parameters
in the model
title : str, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
float_format : str
print format for floats in parameters summary
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary2.Summary : class to hold summary results
"""
from statsmodels.iolib import summary2
smry = summary2.Summary()
smry.add_base(results=self,
alpha=alpha,
float_format=float_format,
xname=xname,
yname=yname,
title=title)
return smry
def summary(self, title=None, alpha=.05):
"""
Summarize the #1lab_results of running MICE.
Parameters
-----------
title : string, optional
Title for the top table. If not None, then this replaces
the default title
alpha : float
Significance level for the confidence intervals
Returns
-------
smry : Summary instance
This holds the summary tables and text, which can be
printed or converted to various output formats.
"""
from statsmodels.iolib import summary2
from statsmodels.compat.collections import OrderedDict
smry = summary2.Summary()
float_format = "%8.3f"
info = OrderedDict()
info["Method:"] = "MICE"
info["Model:"] = self.model_class.__name__
info["Dependent variable:"] = self.endog_names
info["Sample size:"] = "%d" % self.model.data.data.shape[0]
info["Scale"] = "%.2f" % self.scale
info["Num. imputations"] = "%d" % len(self.model.results_list)
smry.add_dict(info, align='l', float_format=float_format)
param = summary2.summary_params(self, alpha=alpha)
param["FMI"] = self.frac_miss_info
smry.add_df(param, float_format=float_format)
smry.add_title(title=title, results=self)
return smry
def summary(self,
show_contrast_L=False,
show_transform_M=False,
show_constant_C=False):
"""
Summary of test results
Parameters
----------
show_contrast_L : bool
Whether to show contrast_L matrix
show_transform_M : bool
Whether to show transform_M matrix
show_constant_C : bool
Whether to show the constant_C
"""
summ = summary2.Summary()
summ.add_title('Multivariate linear model')
for key in self.results:
summ.add_dict({'': ''})
df = self.results[key]['stat'].copy()
df = df.reset_index()
c = df.columns.values
c[0] = key
df.columns = c
df.index = ['', '', '', '']
summ.add_df(df)
if show_contrast_L:
summ.add_dict({key: ' contrast L='})
df = pd.DataFrame(self.results[key]['contrast_L'],
columns=self.exog_names)
summ.add_df(df)
if show_transform_M:
summ.add_dict({key: ' transform M='})
df = pd.DataFrame(self.results[key]['transform_M'],
index=self.endog_names)
summ.add_df(df)
if show_constant_C:
summ.add_dict({key: ' constant C='})
df = pd.DataFrame(self.results[key]['constant_C'])
summ.add_df(df)
return summ
def summary(self):
df = pd.DataFrame()
m = self.model.k_fep + self.model.k_vcp
df["Type"] = (["M" for k in range(self.model.k_fep)] +
["V" for k in range(self.model.k_vcp)])
df["Post. Mean"] = self.params[0:m]
if self._cov_params.ndim == 2:
v = np.diag(self._cov_params)[0:m]
df["Post. SD"] = np.sqrt(v)
else:
df["Post. SD"] = np.sqrt(self._cov_params[0:m])
# Convert variance parameters to natural scale
df["SD"] = np.exp(df["Post. Mean"])
df["SD (LB)"] = np.exp(df["Post. Mean"] - 2 * df["Post. SD"])
df["SD (UB)"] = np.exp(df["Post. Mean"] + 2 * df["Post. SD"])
df["SD"] = ["%.3f" % x for x in df.SD]
df["SD (LB)"] = ["%.3f" % x for x in df["SD (LB)"]]
df["SD (UB)"] = ["%.3f" % x for x in df["SD (UB)"]]
df.loc[df.index < self.model.k_fep, "SD"] = ""
df.loc[df.index < self.model.k_fep, "SD (LB)"] = ""
df.loc[df.index < self.model.k_fep, "SD (UB)"] = ""
df.index = self.model.fep_names + self.model.vcp_names
summ = summary2.Summary()
summ.add_title(self.model.family.__class__.__name__ +
" Mixed GLM Results")
summ.add_df(df)
summ.add_text(
"Parameter types are mean structure (M) and variance structure (V)"
)
summ.add_text(
"Variance parameters are modeled as log standard deviations")
return summ
def summary(self, title=None, alpha=.05):
"""
Summarize the results of running multiple imputation.
Parameters
----------
title : str, optional
Title for the top table. If not None, then this replaces
the default title
alpha : float
Significance level for the confidence intervals
Returns
-------
smry : Summary instance
This holds the summary tables and text, which can be
printed or converted to various output formats.
"""
from statsmodels.iolib import summary2
smry = summary2.Summary()
float_format = "%8.3f"
info = {}
info["Method:"] = "MI"
info["Model:"] = self.mi.model.__name__
info["Dependent variable:"] = self._model.endog_names
info["Sample size:"] = "%d" % self.mi.imp.data.shape[0]
info["Num. imputations"] = "%d" % self.mi.nrep
smry.add_dict(info, align='l', float_format=float_format)
param = summary2.summary_params(self, alpha=alpha)
param["FMI"] = self.fmi
smry.add_df(param, float_format=float_format)
smry.add_title(title=title, results=self)
return smry