statsmodels.genmod.generalized_linear_model.GLM¶

class
statsmodels.genmod.generalized_linear_model.
GLM
(endog, exog, family=None, offset=None, exposure=None, freq_weights=None, var_weights=None, missing='none', **kwargs)[source]¶ Generalized Linear Models class
GLM inherits from statsmodels.base.model.LikelihoodModel
Parameters:  endog (arraylike) – 1d array of endogenous response variable. This array can be 1d or 2d. Binomial family models accept a 2d array with two columns. If supplied, each observation is expected to be [success, failure].
 exog (arraylike) – A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user (models specified using a formula include an intercept by default). See statsmodels.tools.add_constant.
 family (family class instance) – The default is Gaussian. To specify the binomial distribution family = sm.family.Binomial() Each family can take a link instance as an argument. See statsmodels.family.family for more information.
 offset (arraylike or None) – An offset to be included in the model. If provided, must be an array whose length is the number of rows in exog.
 exposure (arraylike or None) – Log(exposure) will be added to the linear prediction in the model. Exposure is only valid if the log link is used. If provided, it must be an array with the same length as endog.
 freq_weights (arraylike) – 1d array of frequency weights. The default is None. If None is selected or a blank value, then the algorithm will replace with an array of 1’s with length equal to the endog. WARNING: Using weights is not verified yet for all possible options and results, see Notes.
 var_weights (arraylike) – 1d array of variance (analytic) weights. The default is None. If None is selected or a blank value, then the algorithm will replace with an array of 1’s with length equal to the endog. WARNING: Using weights is not verified yet for all possible options and results, see Notes.
 missing (str) – Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’

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.

freq_weights
¶ array – See Parameters.

var_weights
¶ array – See Parameters.

mu
¶ array – The estimated mean response of the transformed variable.

n_trials
¶ array – See Parameters.

normalized_cov_params
¶ array – p x p normalized covariance of the design / exogenous data.

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.
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
Notes
Only the following combinations make sense for family and link:
Family ident log logit probit cloglog pow opow nbinom loglog logc Gaussian x x x x x x 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 Tweedie 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.
Statsmodels supports two separte definitions of weights: frequency weights and variance weights.
Frequency weights produce the same results as repeating observations by the frequencies (if those are integers). Frequency weights will keep the number of observations consistent, but the degrees of freedom will change to reflect the new weights.
Variance weights (referred to in other packages as analytic weights) are used when
endog
represents an an average or mean. This relies on the assumption that that the inverse variance scales proportionally to the weight–an observation that is deemed more credible should have less variance and therefore have more weight. For thePoisson
family–which assumes that occurences scale proportionally with time–a natural practice would be to use the amount of time as the variance weight and setendog
to be a rate (occurrances per period of time). Similarly, using a compound Poisson family, namelyTweedie
, makes a similar assumption about the rate (or frequency) of occurences having variance proportional to time.Both frequency and variance weights are verified for all basic results with nonrobust or heteroscedasticity robust
cov_type
. Other robust covariance types have not yet been verified, and at least the small sample correction is currently not based on the correct total frequency count.Currently, all residuals are not weighted by frequency, although they may incorporate
n_trials
forBinomial
andvar_weights
Residual Type Applicable weights Anscombe var_weights
Deviance var_weights
Pearson var_weights
andn_trials
Reponse n_trials
Working n_trials
WARNING: Loglikelihood and deviance are not valid in models where scale is equal to 1 (i.e.,
Binomial
,NegativeBinomial
, andPoisson
). If variance weights are specified, then results such asloglike
anddeviance
are based on a quasilikelihood interpretation. The loglikelihood is not correctly specified in this case, and statistics based on it, such AIC or likelihood ratio tests, are not appropriate.
df_model
float – Model degrees of freedom is equal to p  1, where p is the number of regressors. Note that the intercept is not reported as a degree of freedom.

df_resid
float – Residual degrees of freedom is equal to the number of observation n minus the number of regressors p.

endog
array – See above. Note that endog is a reference to the data so that if data is already an array and it is changed, then endog changes as well.

exposure
¶ arraylike – Include ln(exposure) in model with coefficient constrained to 1. Can only be used if the link is the logarithm function.

exog
array – See above. Note that exog is a reference to the data so that if data is already an array and it is changed, then exog changes as well.

freq_weights
array – See above. Note that freq_weights is a reference to the data so that if data is already an array and it is changed, then freq_weights changes as well.

var_weights
array – See above. Note that var_weights is a reference to the data so that if data is already an array and it is changed, then var_weights changes as well.

iteration
¶ int – The number of iterations that fit has run. Initialized at 0.

family
family class instance – The distribution family of the model. Can be any family in statsmodels.families. Default is Gaussian.

mu
array – The mean response of the transformed variable. mu is the value of the inverse of the link function at lin_pred, where lin_pred is the linear predicted value of the WLS fit of the transformed variable. mu is only available after fit is called. See statsmodels.families.family.fitted of the distribution family for more information.

n_trials
array – See above. Note that n_trials is a reference to the data so that if data is already an array and it is changed, then n_trials changes as well. n_trials is the number of binomial trials and only available with that distribution. See statsmodels.families.Binomial for more information.

normalized_cov_params
array – The p x p normalized covariance of the design / exogenous data. This is approximately equal to (X.T X)^(1)

offset
¶ arraylike – Include offset in model with coefficient constrained to 1.

scale
float – The estimate of the scale / dispersion of the model fit. Only available after fit is called. See GLM.fit and GLM.estimate_scale for more information.

scaletype
str – The scaling used for fitting the model. This is only available after fit is called. The default is None. See GLM.fit for more information.

weights
array – The value of the weights after the last iteration of fit. Only available after fit is called. See statsmodels.families.family for the specific distribution weighting functions.
Methods
estimate_scale
(mu)Estimates the dispersion/scale. estimate_tweedie_power
(mu[, method, low, high])Tweedie specific function to estimate scale and the variance parameter. fit
([start_params, maxiter, method, tol, …])Fits a generalized linear model for a given family. fit_constrained
(constraints[, start_params])fit the model subject to linear equality constraints fit_regularized
([method, alpha, …])Return a regularized fit to a linear regression model. from_formula
(formula, data[, subset, drop_cols])Create a Model from a formula and dataframe. get_distribution
(params[, scale, exog, …])Returns a random number generator for the predictive distribution. hessian
(params[, scale, observed])Hessian, second derivative of loglikelihood function hessian_factor
(params[, scale, observed])Weights for calculating Hessian information
(params[, scale])Fisher information matrix. initialize
()Initialize a generalized linear model. loglike
(params[, scale])Evaluate the loglikelihood for a generalized linear model. loglike_mu
(mu[, scale])Evaluate the loglikelihood for a generalized linear model. predict
(params[, exog, exposure, offset, linear])Return predicted values for a design matrix score
(params[, scale])score, first derivative of the loglikelihood function score_factor
(params[, scale])weights for score for each observation score_obs
(params[, scale])score first derivative of the loglikelihood for each observation. score_test
(params_constrained[, …])score test for restrictions or for omitted variables Attributes
endog_names
Names of endogenous variables exog_names
Names of exogenous variables