Stata 15 help for xtologit

[XT] xtologit -- Random-effects ordered logistic models

Syntax

xtologit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- 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 lrmodel perform the likelihood-ratio model test instead of the default Wald test nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Integration intmethod(intmethod) integration method; intmethod may be mvaghermite (the default) or ghermite intpoints(#) use # quadrature points; default is intpoints(12) Maximization maximize_options control the maximization process; seldom used

startgrid(numlist) improve starting value of the random-intercept parameter by performing a grid search nodisplay suppress display of header and coefficients coeflegend display legend instead of statistics ------------------------------------------------------------------------- A panel variable must be specified; see xtset. indepvars may contain factor variables; see fvvarlist. depvar and indepvars may contain time-series operators; see tsvarlist. by, fp, and statsby are allowed; see prefix. fweights, iweights, and pweights are allowed; see weight. startgrid(), nodisplay, and coeflegend do not appear in the dialog box. See [XT] xtologit postestimation for features available after estimation.

Menu

Statistics > Longitudinal/panel data > Ordinal outcomes > Logistic regression (RE)

Description

xtologit fits random-effects ordered logistic models. The actual values taken on by the dependent variable are irrelevant, although larger values are assumed to correspond to "higher" outcomes. The conditional distribution of the dependent variable given the random effects is assumed to be multinomial with success probability determined by the logistic cumulative distribution function.

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 (oim), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [XT] vce_options.

Specifying vce(robust) is equivalent to specifying vce(cluster panelvar); see xtologit and the robust VCE estimator in Methods and formulas of [XT] xtologit.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

or reports the estimated coefficients transformed to odds ratios, that is, e^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.

lrmodel, nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+-------------+ ----+ Integration +------------------------------------------------------

intmethod(intmethod), intpoints(#); see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. These options are seldom used.

The following options are available with xtologit but are not shown in the dialog box:

startgrid(numlist) performs a grid search to improve the starting value of the random-intercept parameter. No grid search is performed by default unless the starting value is found to not be feasible; in this case, xtologit runs startgrid(0.1 1 10) and chooses the value that works best. You may already be using a default form of startgrid() without knowing it. If you see xtologit displaying Grid node 1, Grid node 2, ... following Grid node 0 in the iteration log, that is xtologit doing a default search because the original starting value was not feasible.

nodisplay is for programmers. It suppresses the display of the header and the coefficients.

coeflegend; see [R] estimation options.

Technical note

The random-effects logit model is calculated using quadrature, which is an approximation whose accuracy depends partially on the number of integration points used. We can use the quadchk command to see if changing the number of integration points affects the results. If the results change, the quadrature approximation is not accurate given the number of integration points. Try increasing the number of integration points using the intpoints() option and again run quadchk. Do not attempt to interpret the results of estimates when the coefficients reported by quadchk differ substantially. See [XT] quadchk for details and [XT] xtprobit for an example.

Because the xtologit likelihood function is calculated by Gauss-Hermite quadrature, on large problems, the computations can be slow. Computation time is roughly proportional to the number of points used for the quadrature.

Example

Setup . webuse tvsfpors . xtset school

Random-effects ordered logit regression . xtologit thk prethk cc##tv

Video example

Ordered logistic and probit for panel data

Stored results

xtologit stores the following in e():

Scalars e(N) number of observations e(N_g) number of groups 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 overall model test e(k_dv) number of dependent variables e(k_cat) number of categories e(df_m) model degrees of freedom e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(ll_c) log likelihood, comparison model e(chi2) chi-squared e(chi2_c) chi-squared for comparison test e(N_clust) number of clusters e(sigma_u) panel-level standard deviation e(n_quad) number of quadrature points e(g_min) smallest group size e(g_avg) average group size e(g_max) largest group size e(p) p-value for model test e(rank) rank of e(V) e(rank0) rank of e(V) for constant-only model e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) meglm e(cmd2) xtologit e(cmdline) command as typed e(depvar) name of dependent variable e(covariates) list of covariates e(ivar) variable denoting groups e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(offset) linear offset variable 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(intmethod) integration method e(distrib) Gaussian; the distribution of the random effect 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(properties) b V e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(marginswtype) weight type for margins e(marginswexp) weight expression for margins e(marginsdefault) default predict() specification for margins 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 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


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