Maximum likelihood estimation of random utility discrete
choice models. The software is described in Croissant (2020)
<doi:10.18637/jss.v095.i11> and the underlying methods in
Train (2009) <doi:10.1017/CBO9780511805271>.

Multinomial Logit Models

package-ubuntu-20.04-r-4.0.5#cran#mlogit-1.1-1

<p>specific stuff compared to the model.matrix.dfidx method which
simply applies the Formula method
</p>

## S3 method for class 'dfidx_mlogit'
model.matrix(object, ..., lhs = NULL, rhs = 1, dot = "separate")

<table summary="R argblock">
<tr valign="top">
<td><code>object</code></td>
<td>
<p>the object</p>
</td>
</tr>
<tr valign="top">
<td><code>..., lhs, rhs, dot</code></td>
<td>
<p>see the <code>Formula</code> method</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#mlogit-1.1-1#docs#model.matrix.dfidx_mlogit

`object`	the object
`..., lhs, rhs, dot`	see the `Formula` method

model.matrix.dfidx_mlogit

Description

Usage

Arguments

Author(s)

mlogit

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