mclogit: Conditional Logit Models and Mixed Conditional Logit Models

In mclogit: Mixed Conditional Logit Models

Description

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.

Usage

mclogit(formula, data=parent.frame(), random=NULL,
        subset, weights = NULL, offset=NULL, na.action = getOption("na.action"),
        model = TRUE, x = FALSE, y = TRUE, contrasts=NULL,
        start=NULL,start.theta=NULL,
        control=mclogit.control(...), ...)

Arguments

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 as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which glm is called.
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 NULL or a numeric vector.
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 NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
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 model frame should be included as a component of the returned value.
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 contrasts.arg of model.matrix.default.
control	a list of parameters for the fitting process. See mclogit.control
...	arguments to be passed to mclogit.control

Details

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".

Value

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.

Examples

data(Transport)

summary(mclogit(
  cbind(resp,suburb)~distance+cost,
  data=Transport
  ))

data(electors)

summary(mclogit(
  cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
  random=~1|party.time,
  data=within(electors,party.time<-interaction(party,time))))

Example output

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 

mclogit documentation built on May 2, 2019, 4:41 p.m.