## models.Glm


A generalized linear model, fitted by IRLS.


Usage


``` python
models.Glm()
```


## Parameters


`family: str`  
`"gaussian"`, `"poisson"`, `"gamma"`, `"inverse_gaussian"`, `"binomial"`, `"negative_binomial"` (needs [theta](reserving.ClarkFit.md#prospicio.reserving.ClarkFit.theta)) or `"tweedie"` (needs [power](distributions.Tweedie.md#prospicio.distributions.Tweedie.power)).

`link: str`  
`"identity"`, `"log"`, `"logit"`, `"probit"`, `"cloglog"`, `"inverse"`, `"inverse_squared"` or `"power"` (needs `link_power`); the family's canonical link by default.

`dispersion: str or float`  
`"pearson"`, `"deviance"` or a fixed value. By default 1 for the Poisson, binomial and negative binomial and Pearson's estimate otherwise; `"pearson"` with the Poisson is the over-dispersed (quasi-) Poisson, which accepts negative responses as long as the fitted means stay positive.

`theta: float`  

`power: float`  

`link_power: float`  


## Examples

``` python
>>> from prospicio.models import Design, Glm
>>> d = Design([[1.0] * 4, [0.0, 0.0, 1.0, 1.0]], ["(Intercept)", "young"],
...            offset=[0.0, 0.0, 0.0, 0.0])
>>> fit = Glm("poisson", "log").fit(d, [1.0, 3.0, 4.0, 6.0])
>>> round(fit.coefficients[1], 10) == round(__import__("math").log(5 / 2), 10)
```

True


## Methods

| Name | Description |
|----|----|
| [fit()](#fit) | Fits the model. |

------------------------------------------------------------------------


#### fit()


Fits the model.


Usage


``` python
fit(design, y)
```


##### Parameters


`design: Design`  

`y: list of float`  


##### Returns


`GlmFit`  


##### Raises


`ValueError`  
If the design is collinear, a response is out of the family's range, or IRLS does not converge.