Stata 15 help for xtcloglog

[XT] xtcloglog -- Random-effects and population-averaged cloglog models

Syntax

Random-effects (RE) model

xtcloglog depvar [indepvars] [if] [in] [weight] [, re RE_options]

Population-averaged (PA) model

xtcloglog depvar [indepvars] [if] [in] [weight], pa [PA_options]

RE_options Description ------------------------------------------------------------------------- Model noconstant suppress constant term re use random-effects estimator; the default offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables asis retain perfect predictor variables

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) lrmodel perform the likelihood-ratio model test instead of the default Wald test eform report exponentiated coefficients 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

coeflegend display legend instead of statistics -------------------------------------------------------------------------

PA_options Description ------------------------------------------------------------------------- Model noconstant suppress constant term pa use population-averaged estimator offset(varname) include varname in model with coefficient constrained to 1 asis retain perfect predictor variables

Correlation corr(correlation) within-panel correlation structure force estimate even if observations unequally spaced in time

SE/Robust vce(vcetype) vcetype may be conventional, robust, bootstrap, or jackknife nmp use divisor N - P instead of the default N scale(parm) override the default scale parameter; parm may be x2, dev, phi, or #

Reporting level(#) set confidence level; default is level(95) eform report exponentiated coefficients display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Optimization optimize_options control the optimization process; seldom used

coeflegend display legend instead of statistics -------------------------------------------------------------------------

correlation Description ------------------------------------------------------------------------- exchangeable exchangeable; the default independent independent unstructured unstructured fixed matname user-specified ar # autoregressive of order # stationary # stationary of order # nonstationary # nonstationary of order # -------------------------------------------------------------------------

A panel variable must be specified. For xtcloglog, pa, correlation structures other than exchangeable and independent require that a time variable also be specified. Use xtset. indepvars may contain factor variables; see fvvarlist. by, mi estimate, and statsby are allowed; see prefix. fp is allowed for the random-effects model. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. iweights, fweights, and pweights are allowed for the population-averaged model, and iweights are allowed for the random-effects model; see weight. Weights must be constant within panel. coeflegend does not appear in the dialog box. See [XT] xtcloglog postestimation for features available after estimation.

Menu

Statistics > Longitudinal/panel data > Binary outcomes > Complementary log-log regression (RE, PA)

Description

xtcloglog fits population-averaged and random-effects complementary log-log (cloglog) models for a binary dependent variable. Complementary log-log models are typically used when one of the outcomes is rare relative to the other.

Options for RE model

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

re requests the random-effects estimator, which is the default.

offset(varname), constraints(constraints), collinear; see [R] estimation options.

asis forces retention of perfect predictor variables and their associated, perfectly predicted observations and may produce instabilities in maximization; see [R] probit.

+-----------+ ----+ 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 xtcloglog, re and the robust VCE estimator in Methods and formulas of [XT] xtcloglog.

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

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

eform displays the exponentiated coefficients and corresponding standard errors and confidence intervals.

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 option is available with xtcloglog but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Options for PA model

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

pa requests the population-averaged estimator.

offset(varname); see [R] estimation options.

asis forces retention of perfect predictor variables and their associated, perfectly predicted observations and may produce instabilities in maximization; see [R] probit.

+-------------+ ----+ Correlation +------------------------------------------------------

corr(correlation) specifies the within-panel correlation structure; the default corresponds to the equal-correlation model, corr(exchangeable).

When you specify a correlation structure that requires a lag, you indicate the lag after the structure's name with or without a blank; for example, corr(ar 1) or corr(ar1).

If you specify the fixed correlation structure, you specify the name of the matrix containing the assumed correlations following the word fixed, for example, corr(fixed myr).

force specifies that estimation be forced even though the time variable is not equally spaced. This is relevant only for correlation structures that require knowledge of the time variable. These correlation structures require that observations be equally spaced so that calculations based on lags correspond to a constant time change. If you specify a time variable indicating that observations are not equally spaced, the (time dependent) model will not be fit. If you also specify force, the model will be fit, and it will be assumed that the lags based on the data ordered by the time variable are appropriate.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (conventional), that are robust to some kinds of misspecification (robust), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [XT] vce_options.

vce(conventional), the default, uses the conventionally derived variance estimator for generalized least-squares regression.

nmp, scale(x2|dev|phi|#); see [XT] vce_options.

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

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

eform displays the exponentiated coefficients and corresponding standard errors and confidence intervals.

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.

+--------------+ ----+ Optimization +-----------------------------------------------------

optimize_options control the iterative optimization process. These options are seldom used.

iterate(#) specifies the maximum number of iterations. When the number of iterations equals #, the optimization stops and presents the current results, even if convergence has not been reached. The default is iterate(100).

tolerance(#) specifies the tolerance for the coefficient vector. When the relative change in the coefficient vector from one iteration to the next is less than or equal to #, the optimization process is stopped. tolerance(1e-6) is the default.

nolog suppress the display of the iteration log.

trace specifies that the current estimates be printed at each iteration.

The following option is available with xtcloglog but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Technical note

The random-effects 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 xtcloglog 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.

Examples

Setup . webuse union

Random-effects model . xtcloglog union age grade south##c.year

Population-averaged model . xtcloglog union age grade south##c.year, pa

Population-averaged model with robust variance . xtcloglog union age grade south##c.year, pa vce(robust)

Stored results

xtcloglog, re 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(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(rho) rho 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) xtcloglog e(cmdline) command as typed e(depvar) name of dependent variable e(ivar) variable denoting groups e(model) re 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(chi2_ct) Wald or LR; type of model chi-squared test corresponding to e(chi2_c) 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(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(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

xtcloglog, pa stores the following in e():

Scalars e(N) number of observations e(N_g) number of groups e(df_m) model degrees of freedom e(chi2) chi-squared e(p) p-value for model test e(df_pear) degrees of freedom for Pearson chi-squared e(chi2_dev) chi-squared test of deviance e(chi2_dis) chi-squared test of deviance dispersion e(deviance) deviance e(dispers) deviance dispersion e(phi) scale parameter e(g_min) smallest group size e(g_avg) average group size e(g_max) largest group size e(rank) rank of e(V) e(tol) target tolerance e(dif) achieved tolerance e(rc) return code

Macros e(cmd) xtgee e(cmd2) xtcloglog e(cmdline) command as typed e(depvar) name of dependent variable e(ivar) variable denoting groups e(tvar) variable denoting time within groups e(model) pa e(family) binomial e(link) cloglog; link function e(corr) correlation structure e(scale) x2, dev, phi, or #; scale parameter e(wtype) weight type e(wexp) weight expression e(offset) linear offset variable e(chi2type) Wald; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(nmp) nmp, if specified e(properties) b V e(predict) program used to implement predict 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(R) estimated working correlation matrix e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


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