# Generalized Linear Models¶

Generalized linear models currently supports estimation using the one-parameter exponential families.

See Module Reference for commands and arguments.

## Examples¶

# Load modules and data
In : import statsmodels.api as sm

# Instantiate a gamma family model with the default link function.
In : gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma())

In : gamma_results = gamma_model.fit()

In : print(gamma_results.summary())
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable:                      y   No. Observations:                   32
Model:                            GLM   Df Residuals:                       24
Model Family:                   Gamma   Df Model:                            7
Method:                          IRLS   Log-Likelihood:                -83.017
Date:                Fri, 21 Feb 2020   Deviance:                     0.087389
Time:                        13:59:13   Pearson chi2:                   0.0860
No. Iterations:                     6
Covariance Type:            nonrobust
==============================================================================
coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const         -0.0178      0.011     -1.548      0.122      -0.040       0.005
x1          4.962e-05   1.62e-05      3.060      0.002    1.78e-05    8.14e-05
x2             0.0020      0.001      3.824      0.000       0.001       0.003
x3         -7.181e-05   2.71e-05     -2.648      0.008      -0.000   -1.87e-05
x4             0.0001   4.06e-05      2.757      0.006    3.23e-05       0.000
x5         -1.468e-07   1.24e-07     -1.187      0.235   -3.89e-07    9.56e-08
x6            -0.0005      0.000     -2.159      0.031      -0.001   -4.78e-05
x7         -2.427e-06   7.46e-07     -3.253      0.001   -3.89e-06   -9.65e-07
==============================================================================


Detailed examples can be found here:

## Technical Documentation¶

The statistical model for each observation $$i$$ is assumed to be

$$Y_i \sim F_{EDM}(\cdot|\theta,\phi,w_i)$$ and $$\mu_i = E[Y_i|x_i] = g^{-1}(x_i^\prime\beta)$$.

where $$g$$ is the link function and $$F_{EDM}(\cdot|\theta,\phi,w)$$ is a distribution of the family of exponential dispersion models (EDM) with natural parameter $$\theta$$, scale parameter $$\phi$$ and weight $$w$$. Its density is given by

$$f_{EDM}(y|\theta,\phi,w) = c(y,\phi,w) \exp\left(\frac{y\theta-b(\theta)}{\phi}w\right)\,.$$

It follows that $$\mu = b'(\theta)$$ and $$Var[Y|x]=\frac{\phi}{w}b''(\theta)$$. The inverse of the first equation gives the natural parameter as a function of the expected value $$\theta(\mu)$$ such that

$$Var[Y_i|x_i] = \frac{\phi}{w_i} v(\mu_i)$$

with $$v(\mu) = b''(\theta(\mu))$$. Therefore it is said that a GLM is determined by link function $$g$$ and variance function $$v(\mu)$$ alone (and $$x$$ of course).

Note that while $$\phi$$ is the same for every observation $$y_i$$ and therefore does not influence the estimation of $$\beta$$, the weights $$w_i$$ might be different for every $$y_i$$ such that the estimation of $$\beta$$ depends on them.

Distribution

Domain

$$\mu=E[Y|x]$$

$$v(\mu)$$

$$\theta(\mu)$$

$$b(\theta)$$

$$\phi$$

Binomial $$B(n,p)$$

$$0,1,\ldots,n$$

$$np$$

$$\mu-\frac{\mu^2}{n}$$

$$\log\frac{p}{1-p}$$

$$n\log(1+e^\theta)$$

1

Poisson $$P(\mu)$$

$$0,1,\ldots,\infty$$

$$\mu$$

$$\mu$$

$$\log(\mu)$$

$$e^\theta$$

1

Neg. Binom. $$NB(\mu,\alpha)$$

$$0,1,\ldots,\infty$$

$$\mu$$

$$\mu+\alpha\mu^2$$

$$\log(\frac{\alpha\mu}{1+\alpha\mu})$$

$$-\frac{1}{\alpha}\log(1-\alpha e^\theta)$$

1

Gaussian/Normal $$N(\mu,\sigma^2)$$

$$(-\infty,\infty)$$

$$\mu$$

$$1$$

$$\mu$$

$$\frac{1}{2}\theta^2$$

$$\sigma^2$$

Gamma $$N(\mu,\nu)$$

$$(0,\infty)$$

$$\mu$$

$$\mu^2$$

$$-\frac{1}{\mu}$$

$$-\log(-\theta)$$

$$\frac{1}{\nu}$$

Inv. Gauss. $$IG(\mu,\sigma^2)$$

$$(0,\infty)$$

$$\mu$$

$$\mu^3$$

$$-\frac{1}{2\mu^2}$$

$$-\sqrt{-2\theta}$$

$$\sigma^2$$

Tweedie $$p\geq 1$$

depends on $$p$$

$$\mu$$

$$\mu^p$$

$$\frac{\mu^{1-p}}{1-p}$$

$$\frac{\alpha-1}{\alpha}\left(\frac{\theta}{\alpha-1}\right)^{\alpha}$$

$$\phi$$

The Tweedie distribution has special cases for $$p=0,1,2$$ not listed in the table and uses $$\alpha=\frac{p-2}{p-1}$$.

Correspondence of mathematical variables to code:

• $$Y$$ and $$y$$ are coded as endog, the variable one wants to model

• $$x$$ is coded as exog, the covariates alias explanatory variables

• $$\beta$$ is coded as params, the parameters one wants to estimate

• $$\mu$$ is coded as mu, the expectation (conditional on $$x$$) of $$Y$$

• $$g$$ is coded as link argument to the class Family

• $$\phi$$ is coded as scale, the dispersion parameter of the EDM

• $$w$$ is not yet supported (i.e. $$w=1$$), in the future it might be var_weights

• $$p$$ is coded as var_power for the power of the variance function $$v(\mu)$$ of the Tweedie distribution, see table

• $$\alpha$$ is either

• Negative Binomial: the ancillary parameter alpha, see table

• Tweedie: an abbreviation for $$\frac{p-2}{p-1}$$ of the power $$p$$ of the variance function, see table

• Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach. SAGE QASS Series.

• Green, PJ. 1984. “Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives.” Journal of the Royal Statistical Society, Series B, 46, 149-192.

• Hardin, J.W. and Hilbe, J.M. 2007. “Generalized Linear Models and Extensions.” 2nd ed. Stata Press, College Station, TX.

• McCullagh, P. and Nelder, J.A. 1989. “Generalized Linear Models.” 2nd ed. Chapman & Hall, Boca Rotan.

## Module Reference¶

### Model Class¶

 GLM(endog, exog[, family, offset, exposure, …]) Generalized Linear Models

### Results Class¶

 GLMResults(model, params, …[, cov_type, …]) Class to contain GLM results. PredictionResults(predicted_mean, var_pred_mean) Attributes

### Families¶

The distribution families currently implemented are

 Family(link, variance) The parent class for one-parameter exponential families. Binomial([link]) Binomial exponential family distribution. Gamma([link]) Gamma exponential family distribution. Gaussian([link]) Gaussian exponential family distribution. InverseGaussian([link]) InverseGaussian exponential family. NegativeBinomial([link, alpha]) Negative Binomial exponential family. Poisson([link]) Poisson exponential family. Tweedie([link, var_power, eql]) Tweedie family.

The link functions currently implemented are the following. Not all link functions are available for each distribution family. The list of available link functions can be obtained by

>>> sm.families.family.<familyname>.links

 Link A generic link function for one-parameter exponential family. CDFLink([dbn]) The use the CDF of a scipy.stats distribution CLogLog The complementary log-log transform Log The log transform Logit The logit transform NegativeBinomial([alpha]) The negative binomial link function Power([power]) The power transform The Cauchy (standard Cauchy CDF) transform cloglog The CLogLog transform link function. The identity transform The inverse transform The inverse squared transform log The log transform logit Methods nbinom([alpha]) The negative binomial link function. probit([dbn]) The probit (standard normal CDF) transform

### Variance Functions¶

Each of the families has an associated variance function. You can access the variance functions here:

>>> sm.families.<familyname>.variance

 VarianceFunction Relates the variance of a random variable to its mean. constant The call method of constant returns a constant variance, i.e., a vector of ones. Power([power]) Power variance function mu Returns np.fabs(mu) mu_squared Returns np.fabs(mu)**2 mu_cubed Returns np.fabs(mu)**3 Binomial([n]) Binomial variance function binary The binomial variance function for n = 1 NegativeBinomial([alpha]) Negative binomial variance function nbinom Negative Binomial variance function.