Description Usage Arguments Details Value Examples

`mclogit`

fits conditional logit models and mixed conditional
logit models to count data and individual choice data,
where the choice set may vary across choice occasions.

Conditional logit models without random effects are fitted by Fisher-scoring/IWLS. The implementation of mixed conditional logit currently is limited to PQL and random intercepts.

1 2 3 4 5 |

`formula` |
a model formula: a symbolic description of the model to be fitted. The left-hand side contains is expected to be a two-column matrix. The first column contains the choice counts or choice indicators (alternative is chosen=1, is not chosen=0). The second column contains unique numbers for each choice set. If individual-level data is used, choice sets correspond to the individuals, if aggregated data with choice counts are used, choice sets may e.g. correspond to covariate classes within clusters. The right-hand of the formula contains choice predictors. It should be noted that constants are deleted from the formula as are predictors that do not vary within choice sets. |

`data` |
an optional data frame, list or environment (or object
coercible by |

`random` |
an optional formula that specifies the random-effects structure or NULL. |

`weights` |
an optional vector of weights to be used in the fitting
process. Should be |

`offset` |
an optional model offset. Currently only supported for models without random effects. |

`subset` |
an optional vector specifying a subset of observations to be used in the fitting process. |

`na.action` |
a function which indicates what should happen
when the data contain |

`start` |
an optional numerical vector of starting values for the conditional logit parameters. |

`start.theta` |
an optional numerical vector of starting values for the variance parameters. |

`model` |
a logical value indicating whether |

`x, y` |
logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value. |

`contrasts` |
an optional list. See the |

`control` |
a list of parameters for the fitting process.
See |

`...` |
arguments to be passed to |

`mclogit`

tries first to fit the model using the IRLS algorithm of
`glm.fit`

, which has the advantage that
starting values are not needed in most cases. If convergence
cannot achieved, it tries to minimize the deviance using
`optim`

with method "BFGS".

`mclogit`

returns an object of class "mclogit", which has almost the
same structure as an object of class "glm". The difference are
the components `coefficients`

, `residuals`

, `fitted.values`

,
`linear.predictors`

, and `y`

, which are matrices with
number of columns equal to the number of response categories minus one.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

```
Loading required package: Matrix
Iteration 1 - Deviance = 39.74973
Iteration 2 - Deviance = 10.50328
Iteration 3 - Deviance = 9.231325
Iteration 4 - Deviance = 9.227742
Iteration 5 - Deviance = 9.227742
converged
Call:
mclogit(formula = cbind(resp, suburb) ~ distance + cost, data = Transport)
Estimate Std. Error z value Pr(>|z|)
distance -1.43940 0.05318 -27.07 <2e-16 ***
cost -0.97753 0.03987 -24.52 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Null Deviance: 2734
Residual Deviance: 9.228
Number of Fisher Scoring iterations: 5
Number of observations: 1994
Iteration 1 - Deviance = 7377.939
Iteration 2 - Deviance = 4589.544
Iteration 3 - Deviance = 4293.485
Iteration 4 - Deviance = 4277.887
Iteration 5 - Deviance = 4277.808
Iteration 6 - Deviance = 4277.808
converged
Iteration 1 - Deviance = 1876.788
Iteration 2 - Deviance = 1212.004
Iteration 3 - Deviance = 1009.8
Iteration 4 - Deviance = 958.7431
Iteration 5 - Deviance = 949.4332
Iteration 6 - Deviance = 948.1453
Iteration 7 - Deviance = 947.9013
Iteration 8 - Deviance = 947.8442
Iteration 9 - Deviance = 947.8329
Iteration 10 - Deviance = 947.8308
Iteration 11 - Deviance = 947.8305
Iteration 12 - Deviance = 947.8304
Iteration 13 - Deviance = 947.8304
Iteration 14 - Deviance = 947.8304
converged
Call:
mclogit(formula = cbind(Freq, interaction(time, class)) ~ econ.left/class +
welfare/class + auth/class, data = within(electors, party.time <- interaction(party,
time)), random = ~1 | party.time)
Coefficents:
Estimate Std. Error z value Pr(>|z|)
econ.left -0.17223 0.13800 -1.248 0.212
welfare 2.05402 0.21441 9.580 <2e-16 ***
auth 0.08170 0.11820 0.691 0.489
econ.left:classnew.middle -1.66937 0.08804 -18.961 <2e-16 ***
econ.left:classold.middle -2.97243 0.14941 -19.894 <2e-16 ***
classnew.middle:welfare -0.98925 0.06088 -16.248 <2e-16 ***
classold.middle:welfare -1.61549 0.12869 -12.553 <2e-16 ***
classnew.middle:auth -1.39210 0.04679 -29.752 <2e-16 ***
classold.middle:auth 1.45677 0.05817 25.044 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Co-)Variances:
Grouping level: party.time
Estimate Std. Error
(Intercept) (Intercept)
(Intercept) 1.6343 0.1484
Null Deviance: 80580
Residual Deviance: 947.8
Number of Fisher Scoring iterations: 14
Number of observations: 37500
```

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