help xtgee dialog: xtgee
also see: xtgee postestimation
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Title
[XT] xtgee -- Fit population-averaged panel-data models by using GEE
Syntax
xtgee depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
family(family) distribution of depvar
link(link) link function
Model 2
exposure(varname) include ln(varname) in model with coefficient
constrained to 1
offset(varname) include varname in model with coefficient
constrained to 1
noconstant suppress constant term
force estimate even if observations unequally spaced in
time
Correlation
corr(correlation) within-group correlation structure
SE/Robust
vce(vcetype) vcetype may be conventional, robust, bootstrap, or
jackknife
nmp use divisor N-P instead of the default N
rgf multiply the robust variance estimate by
(N-1)/(N-P)
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 spacing and display of omitted variables
and base and empty cells
Optimization
optimize_options control the optimization process; seldom used
+ nodisplay suppress display of header and coefficients
+ coeflegend display coefficients' legend instead of
coefficient table
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+ nodisplay and coeflegend are not shown in the dialog box.
A panel variable must be specified. For xtgee, 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.
depvar and indepvars may contain time-series operators; see tsvarlist.
by, fracpoly, mfp, and statsby are allowed; see prefix.
iweights, fweights, and pweights are allowed; see weight. Weights must
be constant within panel.
See [XT] xtgee postestimation for features available after estimation.
family description
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gaussian Gaussian (normal); family(normal) is a synonym
igaussian inverse Gaussian
binomial[#|varname] Bernoulli/binomial
poisson Poisson
nbinomial[#] negative binomial
gamma gamma
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link description
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identity identity; y=y
log log; ln(y)
logit logit; ln{y/(1-y)}, natural log of the odds
probit probit; inverse Gaussian cumulative
cloglog cloglog; ln{-ln(1-y)}
power[#] power; y^k with k=#; #=1 if not specified
opower[#] odds power; [{y/(1-y)}^k - 1]/k with k=#; #=1 if
not specified
nbinomial negative binomial
reciprocal reciprocal; 1/y
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correlation description
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exchangeable exchangeable
independent independent
unstructured unstructured
fixed matname user-specified
ar # autoregressive of order #
stationary # stationary of order #
nonstationary # nonstationary of order #
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Menu
Statistics > Longitudinal/panel data > Generalized estimating equations
(GEE) > Generalized estimating equations (GEE)
Description
xtgee fits population-averaged panel-data models. In particular, xtgee
fits general linear models and allows you to specify the within-group
correlation structure for the panels.
See logistic estimation commands and [R] regress for lists of related
estimation commands.
Options
+-------+
----+ Model +------------------------------------------------------------
family(family) specifies the distribution of depvar; family(gaussian) is
the default.
link(link) specifies the link function; the default is the canonical link
for the family() specified.
+---------+
----+ Model 2 +----------------------------------------------------------
exposure(varname) and offset(varname) are different ways of specifying
the same thing. exposure() specifies a variable that reflects the
amount of exposure over which the depvar events were observed for
each observation; ln(varname) with coefficient constrained to be 1 is
entered into the regression equation. offset() specifies a variable
that is to be entered directly into the log-link function with its
coefficient constrained to be 1; thus, exposure is assumed to be
e^varname. If you were fitting a Poisson regression model,
family(poisson) link(log), for instance, you would account for
exposure time for specifying offset() containing the log of exposure
time.
noconstant specifies that the linear predictor has no intercept term,
thus forcing it through the origin on the scale defined by the link
function.
force; see [R] estimation options.
+-------------+
----+ Correlation +------------------------------------------------------
corr(correlation); 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, and that use bootstrap or
jackknife methods; see [XT] vce_options.
vce(conventional), the default, uses the conventionally derived
variance estimator for generalized least squares regression.
vce(robust) specifies that the Huber/White/sandwich estimator of
variance is to be used in place of the default GLS variance
estimator; see [XT] xtgee. Use of this option causes xtgee to
produce valid standard errors even if the correlations within group
are not as hypothesized by the specified correlation structure. It
does, however, require that the model correctly specifies the mean.
The resulting standard errors are thus labeled "semirobust" instead
of "robust". Although there is no vce(cluster clustvar) option,
results are as if this option were included and you specified
clustering on the panel variable.
nmp; see [XT] vce_options.
rgf specifies that the robust variance estimate is multiplied by
(N-1)/(N-P), where N = # of observations, and P = # of coefficients
estimated. This option can be used only with family(gaussian) when
vce(robust) is either specified or implied by the use of pweights.
Using this option implies that the robust variance estimate is not
invariant to the scale of any weights used.
scale(x2|dev|phi|#); see [XT] vce_options.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
eform displays the exponentiated coefficients and corresponding standard
erros and confidence intervals as described in maximize. For
family(binomial) link(logit) (i.e., logistic regression),
exponentiation results in odds ratios; for family(poisson) link(log)
(i.e., Poisson regression), exponentiated coefficients are
incidence-rate ratios.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; 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 the convergence tolerance has not been
reached. The default value of iterate() is 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 should be printed at each
iteration.
The following options are available with xtgee but are not shown in the
dialog box:
nodisplay is for programmers. It suppresses the display of the header
and coefficients.
coeflegend; see [R] estimation options.
Examples
Setup
. webuse union
. xtset id year
Fit a logit model
. xtgee union age grade not_smsa south, family(binomial) link(logit)
Fit a probit model with AR(1) correlation
. xtgee union age grade not_smsa south, family(binomial) link(probit)
corr(ar1)
Correlation structures and the allowed spacing of observations within panel
--characteristics allowed--
Unequal
Correlation Unbalanced spacing Gaps
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independent yes yes yes
exchangeable yes yes yes
ar k yes (*) no no
stationary k yes (*) no no
nonstationary k yes (*) no no
unstructured yes yes yes
fixed yes yes yes
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(*) All panels must have at least k+1 obs.
Definitions:
1. Panels are balanced if each has the same number of observations.
2. Panels are equally spaced if the interval between observations is
constant.
3. Panels have gaps if some observations are missing.
Saved results
xtgee saves 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(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(tol) target tolerance
e(dif) achieved tolerance
e(rank) rank of e(V)
e(rc) return code
Macros
e(cmd) xtgee
e(cmdline) command as typed
e(depvar) name of dependent variable
e(ivar) variable denoting groups
e(tvar) time variable
e(family) distribution family
e(link) link function
e(corr) correlation structure
e(scale) x2, dev, phi, or #; scale parameter
e(wtype) weight type
e(wexp) weight expression
e(offset) offset
e(chi2type) Wald; type of model chi-squared test
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(robust_prolog) program to prepare estimates for linearized VCE
computations
e(robust_epilog) program to finalize estimates after linearized VCE
computations
e(crittype) optimization criterion
e(properties) b V
e(estat_cmd) program used to implement estat
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
Also see
Manual: [XT] xtgee
Help: [XT] xtgee postestimation;
[XT] xtlogit, [XT] xtnbreg, [XT] xtpoisson, [XT] xtprobit, [XT]
xtreg, [XT] xtregar, [R] glm, [R] logistic, [R] regress
