help clogit dialogs: clogit svy: clogit
also see: clogit postestimation
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Title
[R] clogit -- Conditional (fixed-effects) logistic regression
Syntax
clogit depvar [indepvars] [if] [in] [weight] , group(
varname) [options]
options description
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Model
* group(varname) matched group variable
offset(varname) include varname in model with coefficient
constrained to 1
constraints(numlist) 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 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, 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(), 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 [R] clogit postestimation for features available after estimation.
Menu
Statistics > Categorical outcomes > Conditional logistic regression
Description
clogit fits what biostatisticians and epidemiologists call conditional
logistic regression for matched case-control groups and what economists
and other social scientists call fixed-effects logit for panel data.
Computationally, these models are the same.
See [R] asclogit if you want to fit McFadden's choice model. Also see
logistic estimation commands for a list of related estimation commands.
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(numlist), 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.
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, 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.
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
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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
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Setup
. webuse union, clear
Fit conditional logistic regression (panel data)
. clogit union age grade not_smsa, group(idcode)
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Saved results
clogit saves 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 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, constant-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) 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) offset
e(chi2type) 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(marginsok) predictions allowed by margins
e(marginsnotok) predictions disallowed by margins
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(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] clogit
Help: [R] clogit postestimation;
[R] asclogit, [R] logistic, [R] mlogit, [R] nlogit, [R] ologit,
[R] scobit, [SVY] svy estimation, [XT] xtgee, [XT] xtlogit
