mclogit  R Documentation 
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 Fisherscoring/IWLS. Models with random effects (mixed conditional logit models) are estimated via maximum likelihood with a simple Laplace aproximation (aka PQL).
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, method = NULL, estimator=c("ML","REML"), dispersion = FALSE, start=NULL, control=if(length(random)) mmclogit.control(...) else mclogit.control(...), ...) ## S3 method for class 'mclogit' update(object, formula., dispersion, ...) ## S3 method for class 'mclogit' summary(object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ...)
formula 
a model formula: a symbolic description of the model to be fitted. The lefthand side should result in a twocolumn 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. The lefthand side can either take the form If individuallevel data is used, choice sets correspond to individuals, if aggregated data with choice counts are used, choice sets usually correspond to covariate classes. The righthand 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 or list of formulas that specify the randomeffects 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. 
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 
method 

estimator 
a character string; either "ML" or "REML", specifies which estimator is to be used/approximated. 
dispersion 
a real number used as dispersion parameter;
a character vector that specifies the method to compute the dispersion;
a logical value – if 
control 
a list of parameters for the fitting process.
See 
... 
arguments to be passed to 
object 
an object that inherits class 
formula. 
a changes to the model formula,
see 
correlation 
logical; see 
symbolic.cor 
logical; see 
mclogit
returns an object of class "mclogit", which has almost the
same structure as an object of class "glm".
Covariates that are constant within choice sets are automatically
dropped from the model formula specified by the formula
argument of mclogit
.
If the model contains random effects, these should
either vary within choice sets (e.g. the levels of a factor that defines the choice sets should not be nested within the levels of factor)
or be random coefficients of covariates that vary within choice sets.
In earlier versions of the package (prior to 0.6) it will lead to a
failure of the model fitting algorithm if these conditions are not
satisfied. Since version 0.6 of the package, the function
mclogit
will complain about such model a misspecification
explicitely.
Agresti, Alan (2002). Categorical Data Analysis. 2nd ed, Hoboken, NJ: Wiley. doi: 10.1002/0471249688
Breslow, N.E. and D.G. Clayton (1993). "Approximate Inference in Generalized Linear Mixed Models". Journal of the American Statistical Association 88 (421): 925. doi: 10.1080/01621459.1993.10594284
Elff, Martin (2009). "Social Divisions, Party Positions, and Electoral Behaviour". Electoral Studies 28(2): 297308. doi: 10.1016/j.electstud.2009.02.002
McFadden, D. (1973). "Conditionial Logit Analysis of Qualitative Choice Behavior". Pp. 105135 in P. Zarembka (ed.). Frontiers in Econometrics. New York: Wiley. https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf
Conditional logit models are also supported by gmnl, mlogit, and survival. survival supports conditional logit models for binary panel data and casecontrol studies. mlogit and gmnl treat conditional logit models from an econometric perspective. Unlike the present package, they focus on the random utility interpretation of discrete choice models and support generalisations of conditional logit models, such as nested logit models, that are intended to overcome the IIA (indipendence from irrelevant alterantives) assumption. Mixed multinomial models are also supported and estimated using simulationbased techniques. Unlike the present package, mixed or randomeffects extensions are mainly intended to fit repeated choices of the same individuals and not aggregated choices of many individuals facing identical alternatives.
data(Transport) summary(mclogit( cbind(resp,suburb)~distance+cost, data=Transport )) # New syntactic sugar: summary(mclogit( respsuburb~distance+cost, data=Transport )) ## Not run: # This takes a bit longer. data(electors) electors < within(electors,{ party.time <interaction(party,time) time.class < interaction(time,class) }) # Time points nested within parties summary(mclogit( Freqtime.class~econ.left/class+welfare/class+auth/class, random=~1party/time, data=electors)) # Partylevel random intercepts and random slopes varying over time points summary(mclogit( Freqtime.class~econ.left/class+welfare/class+auth/class, random=list(~1party,~econ.left+0time), data=electors)) ## End(Not run)
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