Generalized Linear Models class
GLM inherits from statsmodels.LikelihoodModel
Parameters: | endog : array-like
exog : array-like
family : family class instance
missing : str
|
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See also
statsmodels.families.
Notes
Only the following combinations make sense for family and link
+ ident log logit probit cloglog pow opow nbinom loglog logc
Gaussian | x x x
inv Gaussian | x x x
binomial | x x x x x x x x x
Poission | x x x
neg binomial | x x x x
gamma | x x x
Not all of these link functions are currently available.
Endog and exog are references so that if the data they refer to are already arrays and these arrays are changed, endog and exog will change.
Attributes
Examples
>>> import statsmodels.api as sm
>>> data = sm.datasets.scotland.load()
>>> data.exog = sm.add_constant(data.exog)
Instantiate a gamma family model with the default link function.
>>> gamma_model = sm.GLM(data.endog, data.exog,
... family=sm.families.Gamma())
>>> gamma_results = gamma_model.fit()
>>> gamma_results.params
array([-0.01776527, 0.00004962, 0.00203442, -0.00007181, 0.00011185,
-0.00000015, -0.00051868, -0.00000243])
>>> gamma_results.scale
0.0035842831734919055
>>> gamma_results.deviance
0.087388516416999198
>>> gamma_results.pearson_chi2
0.086022796163805704
>>> gamma_results.llf
-83.017202161073527
Attributes
df_model | float | p - 1, where p is the number of regressors including the intercept. |
df_resid | float | The number of observation n minus the number of regressors p. |
endog | array | See Parameters. |
exog | array | See Parameters. |
family | family class instance | A pointer to the distribution family of the model. |
mu | array | The estimated mean response of the transformed variable. |
normalized_cov_params | array | p x p normalized covariance of the design / exogenous data. |
pinv_wexog | array | For GLM this is just the pseudo inverse of the original design. |
scale | float | The estimate of the scale / dispersion. Available after fit is called. |
scaletype | str | The scaling used for fitting the model. Available after fit is called. |
weights | array | The value of the weights after the last iteration of fit. |
Methods
estimate_scale(mu) | Estimates the dispersion/scale. |
fit([maxiter, method, tol, scale]) | Fits a generalized linear model for a given family. |
from_formula(formula, data[, subset]) | Create a Model from a formula and dataframe. |
hessian(params) | The Hessian matrix of the model |
information(params) | Fisher information matrix. |
initialize() | Initialize a generalized linear model. |
loglike(*args) | Loglikelihood function. |
predict(params[, exog, linear]) | Return predicted values for a design matrix |
score(params) | Score matrix. |
Attributes
endog_names | |
exog_names |