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

__Syntax__

__clog__**it** *depvar* [*indepvars*] [*if*] [*in*] [*weight*] **,** __gr__**oup(**
*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
* __gr__**oup(***varname***)** matched group variable
__off__**set(***varname***)** include *varname* in model with coefficient
constrained to 1
__const__**raints(***constraints***)** apply specified linear constraints
__col__**linear** keep collinear variables

SE/Robust
**vce(***vcetype***)** *vcetype* may be **oim**, __r__**obust**, __cl__**uster** *clustvar*,
**opg**, __boot__**strap**, or __jack__**knife**
**nonest** do not check that panels are nested within
clusters

Reporting
__l__**evel(***#***)** set confidence level; default is **level(95)**
**or** report odds ratios
__nocnsr__**eport** 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

__coefl__**egend** 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.
**fweight**s, **iweight**s, and **pweight**s 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**, __nopv__**alues**, __noomit__**ted**, **vsquish**, __noempty__**cells**,
__base__**levels**, __allbase__**levels**, __nofvlab__**el**, **fvwrap(***#***)**, **fvwrapon(***style***)**,
**cformat(***%fmt***)**, **pformat(%***fmt***)**, **sformat(%***fmt***)**, and **nolstretch**; see **[R]**
**estimation options**.

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

*maximize_options*: __dif__**ficult**, __tech__**nique(***algorithm_spec***)**, __iter__**ate(***#***)**,
[__no__]__lo__**g**, __tr__**ace**, __grad__**ient**, **showstep**, __hess__**ian**, __showtol__**erance**,
__tol__**erance(***#***)**, __ltol__**erance(***#***)**, __nrtol__**erance(***#***)**, __nonrtol__**erance**, 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.