Stata 15 help for clogit

[R] clogit -- Conditional (fixed-effects) logistic regression

Syntax

clogit depvar [indepvars] [if] [in] [weight] , group( varname) [options]

depvar is treated as binary regardless of values; depvar equal to nonzero and nonmissing (typically equal to 1) indicates a positive outcome, whereas depvar equal to 0 indicates a negative outcome.

options Description ------------------------------------------------------------------------- Model * group(varname) matched group variable 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, opg, bootstrap, or jackknife nonest do not check that panels are nested within clusters

Reporting level(#) set confidence level; default is level(95) or report odds ratios 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

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * group(varname) is required. indepvars may contain factor variables; see fvvarlist. bayes, bootstrap, by, fp, jackknife, mfp, mi estimate, nestreg, rolling, statsby, stepwise, and svy are allowed; see prefix. For more details, see [BAYES] bayes: clogit. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. Weights are not allowed with the bootstrap prefix. vce(), nonest, and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed (see weight), but they are interpreted to apply to groups as a whole, not to individual observations. See Use of weights in [R] clogit. coeflegend does not appear in the dialog box. See [R] clogit postestimation for features available after estimation.

Menu

Statistics > Categorical outcomes > Conditional logistic regression

Description

clogit fits a conditional logistic regression model for matched case-control data, also known as a fixed-effects logit model for panel data. clogit can compute robust and cluster-robust standard errors and adjust results for complex survey designs.

See [R] asclogit if you want to fit McFadden's choice model (McFadden 1974).

Options

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

group(varname) is required; it specifies an identifier variable (numeric or string) for the matched groups. strata(varname) is a synonym for group().

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, opg), 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 [R] vce_option.

nonest, available only with vce(cluster clustvar), prevents checking that matched groups are nested within clusters. It is the user's responsibility to verify that the standard errors are theoretically correct.

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

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

or reports the estimated coefficients transformed to odds ratios, that is, 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: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; 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.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

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

coeflegend; see [R] estimation options.

Examples

--------------------------------------------------------------------------- Setup . webuse lowbirth2

Fit conditional logistic regression (matched case-control data) . clogit low lwt smoke ptd ht ui i.race, group(pairid)

Replay results, reporting odds ratios rather than coefficients . clogit, or

--------------------------------------------------------------------------- Setup . webuse union

Fit conditional logistic regression (panel data) . clogit union age grade not_smsa, group(idcode) ---------------------------------------------------------------------------

Stored results

clogit stores the following in e():

Scalars e(N) number of observations e(N_drop) number of observations dropped because of all positive or all negative outcomes e(N_group_drop) number of groups dropped because of all positive or all negative outcomes e(k) number of 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(r2_p) pseudo-R-squared e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(chi2) chi-squared e(p) p-value for model test 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) clogit e(cmdline) command as typed e(depvar) name of dependent variable e(group) name of group() variable e(multiple) multiple if multiple positive outcomes within group 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(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(marginsnotok) predictions disallowed by 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 (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

Reference

McFadden, D. L. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, ed. P. Zarembka, 105-142. New York: Academic Press.


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