help ologit dialogs: ologit svy: ologit
also see: ologit postestimation
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Title
[R] ologit -- Ordered logistic regression
Syntax
ologit depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
offset(varname) include varname in model with coefficient
constrained to 1
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
SE/Robust
vce(vcetype) vcetype may be oim, robust, cluster
clustvar, bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
or report odds ratios
nocnsreport do not display constraints
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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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, fracpoly, jackknife, mfp, mi estimate, nestreg, rolling,
statsby, stepwise, and svy are allowed; see prefix.
vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate
prefix.
Weights are not allowed with the bootstrap prefix.
vce() and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
See [R] ologit postestimation for features available after estimation.
Menu
Statistics > Ordinal outcomes > Ordered logistic regression
Description
ologit fits ordered logit models of ordinal variable depvar on the
independent variables indepvars. The actual values taken on by the
dependent variable are irrelevant, except that larger values are assumed
to correspond to "higher" outcomes.
See logistic estimation commands for a list of related estimation
commands.
Options
+-------+
----+ Model +------------------------------------------------------------
offset(varname), constraints(constraints), collinear; see [R] estimation
options.
+-----------+
----+ 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.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
or reports the estimated coefficients transformed to odds ratios, i.e.,
exp(b) rather than b. Standard errors and confidence intervals are
similarly transformed. This option affects how results are
displayed, not how they are estimated. or may be specified at
estimation or when replaying previously estimated results.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
from(init_specs); see [R] maximize. These options are seldom used.
The following option is available with ologit but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Example
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Setup
. webuse fullauto
Ordered logistic regression
. ologit rep77 foreign length mpg
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Setup
. webuse nhanes2f
. svyset psuid [pw=finalwgt], strata(stratid)
Ordered logistic regression with survey data
. svy: ologit health female black age c.age#c.age
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Saved results
ologit saves the following in e():
Scalars
e(N) number of observations
e(N_cd) number of completely determined observations
e(k_cat) number of categories
e(k) number of parameters
e(k_aux) number of auxiliary parameters
e(k_eq) number of equations in e(b)
e(k_eq_model) number of equations in model Wald test
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
e(df_m) model degrees of freedom
e(r2_p) pseudo-R-squared
e(ll) log likelihood
e(ll_0) log likelihood, contant-only model
e(N_clust) number of clusters
e(chi2) chi-squared
e(p) significance
e(rank) rank of e(V)
e(ic) number of iterations
e(rc) return code
e(converged) 1 if converged, 0 otherwise
Macros
e(cmd) ologit
e(cmdline) command as typed
e(depvar) name of dependent 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(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(opt) type of optimization
e(which) max or min; whether optimizer is to perform
maximization or minimization
e(ml_method) type of ml method
e(user) name of likelihood-evaluator program
e(technique) maximization technique
e(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
e(marginsprop) signals to the margins command
e(asbalanced) factor variables fvset as asbalanced
e(asobserved) factor variables fvset as asobserved
Matrices
e(b) coefficient vector
e(Cns) constraints matrix
e(ilog) iteration log (up to 20 iterations)
e(gradient) gradient vector
e(cat) category values
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] ologit
Help: [R] ologit postestimation;
[R] clogit, [R] logistic, [R] logit, [R] mlogit, [R] oprobit,
[R] rologit, [R] slogit, [SVY] svy estimation
