simGLM {alpaca} | R Documentation |

## Generate an artificial data set for some GLM's with two-way fixed effects

### Description

Constructs an artificial data set with `n`

cross-sectional units observed for `t`

time
periods for logit, poisson, or gamma models. The “true” linear predictor
(`\boldsymbol{\eta}`

) is generated as follows:

```
\eta_{it} = \mathbf{x}_{it}^{\prime} \boldsymbol{\beta} +
\alpha_{i} + \gamma_{t} \, ,
```

where `\mathbf{X}`

consists of three independent standard normally distributed regressors.
Both parameter referring to the unobserved heterogeneity (`\alpha_{i}`

and
`\gamma_{t}`

) are generated as iid. standard normal and the structural parameters are
set to `\boldsymbol{\beta} = [1, - 1, 1]^{\prime}`

.

**Note:** The poisson and gamma model are based on the logarithmic link function.

### Usage

```
simGLM(n = NULL, t = NULL, seed = NULL, model = c("logit", "poisson", "gamma"))
```

### Arguments

`n` |
a strictly positive integer equal to the number of cross-sectional units. |

`t` |
a strictly positive integer equal to the number of time periods. |

`seed` |
a seed to ensure reproducibility. |

`model` |
a string equal to |

### Value

The function `simGLM`

returns a data.frame with 6 variables.

### See Also

*alpaca*version 0.3.4 Index]