help rologit dialog: rologit
also see: rologit postestimation
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Title
[R] rologit -- Rank-ordered logistic regression
Syntax
rologit depvar indepvars [if] [in] [weight] , group(varname)
[options]
options description
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Model
* group(varname) identifier variable that links the alternatives
offset(varname) include varname in model with coefficient
constrained to 1
incomplete(#) use # to code unranked alternatives; default is
incomplete(0)
reverse reverse the preference order
notestrhs keep right-hand-side variables that do not vary
within group
ties(spec) method to handle ties: exactm, breslow, efron, or
none
SE/Robust
vce(vcetype) vcetype may be oim, robust, cluster clustvar,
bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
display_options control spacing and display of omitted variables and
base and empty cells
Maximization
maximize_options control the maximization process; seldom used
+ coeflegend display coefficients' legend instead of coefficient
table
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*group(varname) is required.
+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
bootstrap, by, jackknife, rolling, and statsby are allowed; see prefix.
Weights are not allowed with the bootstrap prefix.
fweights, iweights, and pweights are allowed, except with ties(efron);
see weight.
See [R] rologit postestimation for features available after estimation.
Menu
Statistics > Ordinal outcomes > Rank-ordered logistic regression
Description
rologit fits the rank-ordered logistic regression model by maximum
likelihood (Beggs, Cardell, and Hausman 1981). This model is also known
as the Plackett-Luce model (Marden 1995), as the exploded logit model
(Punj and Staelin 1978), and as the choice-based method of conjoint
analysis (Hair et al. 2010).
rologit expects the data to be in long form, similar to clogit, in which
each of the ranked alternatives forms an observation; all observations
related to an individual are linked together by the variable that you
specify in the group() option. The distinction from clogit is that
depvar in rologit records the rankings of the alternatives, whereas for
clogit, depvar marks only the best alternative by a value not equal to
zero. rologit interprets equal scores of depvar as ties. The ranking
information may be incomplete "at the bottom" (least preferred
alternatives). That is, unranked alternatives may be coded as 0 or as a
common value that may be specified with the incomplete() option.
If your data record only the unique alternative, rologit fits the same
model as clogit.
Options
+-------+
----+ Model +------------------------------------------------------------
group(varname) is required, and it specifies the identifier variable
(numeric or string) that links the alternatives for an individual,
which have been compared and rank ordered with respect to one
another.
offset(varname); see [R] estimation options.
incomplete(#) specifies the numeric value used to code alternatives that
are not ranked. It is assumed that unranked alternatives are less
preferred than the ranked alternatives (i.e., the data record the
ranking of the most preferred alternatives). It is not assumed that
subjects are indifferent between the unranked alternatives. #
defaults to 0.
reverse specifies that in the preference order, a higher number means a
less attractive alternative. The default is that higher values
indicate more attractive alternatives. The rank-order logit model is
not symmetric in the sense that reversing the ordering simply leads
to a change in the signs of the coefficients.
notestrhs suppresses the test that the independent variables vary within
(at least some of) the groups. Effects of variables that are always
constant are not identified. For instance, a rater's gender cannot
directly affect his or her rankings; it could affect the rankings
only via an interaction with a variable that does vary between
alternatives.
ties(spec) specifies the method for handling ties (indifference between
alternatives) (see [ST] stcox for details):
exactm exact marginal likelihood (default)
breslow Breslow's method (default if pweights specified)
efron Efron's method (default if robust VCE)
none no ties allowed
+-----------+
----+ SE/Robust +--------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types that are derived from asymptotic theory, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
If ties(exactm) is specified, vcetype may be only oim, bootstrap, or
jackknife.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: iterate(#), trace, [no]log, tolerance(#),
ltolerance(#), nrtolerance(#), nonrtolerance; see [R] maximize.
These options are seldom used.
The following option is available with rologit but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Example
You have data in which subjects ranked up to four options. rologit
requires that the data are in "long format", in which the responses of
one subject are recorded in different records (observations).
caseid depvar option x1 x2 male
1 4 1 1 0 0
1 2 2 0 1 0
1 3 3 0 0 0
1 1 4 1 1 0
2 1 1 3 0 0
2 3 2 0 1 0
2 3 3 2 1 0
2 4 4 1 2 0
3 1 1 3 1 1
3 3 2 1 1 1
3 4 4 0 1 1
4 2 1 1 1 1
4 1 2 1 1 1
4 0 3 0 1 1
4 0 4 1 0 1
where 0 indicates that subject 4 only specified his two most favorable
alternatives. In this example
subject 1 has ranking
option_1 > option_3 > option_2 > option_4
subject 2 has a ranking with ties,
option_4 > option_2 == option_3 > option_1
subject 3 ranked a subset of alternatives, ignoring option 3,
option_4 > option_2 > option_1
subject 4 had an incomplete ranking
option_1 > option_2 > (option_3,option_4)
Subject 4 ranked option_1 highest among all four options, and ranked
option_2 highest among the remaining three options. His preference
ordering among option_3 and option_4, however, is not known.
. webuse rologitxmpl2
You can fit a rank-ordered logit model for up to four alternatives as
. rologit depvar x1 x2, group(caseid)
More complicated models may be formulated as well. We can perform a
likelihood-ratio test that men and women rank the options in the same way
(note that the main effect of gender is not identified),
. estimates store base
. gen sx1 = x1 * (male==1)
. gen sx2 = x2 * (male==1)
. rologit depvar x1 x2 male#c.x1 male#c.x2, group(caseid)
. estimates store full
. lrtest base full
A note on data organization
Sometimes your data will be in a "wide format" in which the ranking of
options are described in a series of variables, rather than in different
observations that are associated with one subject.
caseid opt1 opt2 opt3 opt4
1 4 2 3 1
2 1 3 3 4
3 1 3 . 4
4 2 1 0 0
You may want to verify that this information is identical to the data in
long format listed above. The Stata command reshape makes the
transformation between "long" and "wide" formats quite simple,
. reshape long opt, i(caseid) j(option)
. drop if missing(opt)
Saved results
rologit saves the following in e():
Scalars
e(N) number of observations
e(ll_0) log likelihood of the null model ("all rankings are
equiprobable")
e(ll) log likelihood
e(df_m) model degrees of freedom
e(chi2) chi-squared
e(r2_p) pseudo-R^2
e(N_g) number of groups
e(g_min) minimum group size
e(g_avg) average group size
e(g_max) maximum group size
e(code_inc) value for incomplete preferences
e(N_clust) number of clusters
e(rank) rank of e(V)
Macros
e(cmd) rologit
e(cmdline) command as typed
e(depvar) name of dependent variable
e(group) name of group() variable
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(clustvar) name of cluster variable
e(offset) offset
e(chi2type) Wald or LR; type of model chi-squared test
e(ties) breslow, efron, exactm
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
e(marginsok) predictions allowed by margins
e(marginsnotok) predictions disallowed by margins
e(asbalanced) factor variables fvset as asbalanced
e(asobserved) factor variables fvset as asobserved
Matrices
e(b) coefficient vector
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
References
Beggs, S., S. Cardell, and J. A. Hausman. 1981. Assessing the potential
demand for electric cars. Journal of Econometrics 17: 1-19.
Hair, Jr. J. F., W. C. Black, and B. J. Babin, and R. E. Anderson. 2010.
Multivariate Data Analysis. 7th ed. Upper Saddle River, NJ: Pearson.
Marden, J. I. 1995. Analyzing and Modeling Rank Data. London: Chapman &
Hall.
Punj, G. N., and R. Staelin. 1978. The choice process for graduate
business schools. Journal of Marketing Research 15: 588-598.
Also see
Manual: [R] rologit
Help: [R] rologit postestimation;
[R] clogit, [R] logistic, [R] mlogit, [R] nlogit, [R] slogit
