Stata 15 help for xtgee

[XT] xtgee -- Fit population-averaged panel-data models by using GEE

Syntax

xtgee depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- 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 asis retain perfect predictor variables 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 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

nodisplay suppress display of header and coefficients coeflegend display legend instead of statistics ------------------------------------------------------------------------- A panel variable must be specified. 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, mfp, mi estimate, and statsby are allowed; see prefix. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. iweights, fweights, and pweights are allowed; see weight. Weights must be constant within panel. nodisplay and coeflegend do not appear in the dialog box. See [XT] xtgee postestimation for features available after estimation.

family Description ------------------------------------------------------------------------- gaussian Gaussian (normal); family(normal) is a synonym igaussian inverse Gaussian binomial[#|varname] Bernoulli/binomial poisson Poisson nbinomial[#] negative binomial gamma gamma -------------------------------------------------------------------------

link Description ------------------------------------------------------------------------- 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 clog-log; 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 -------------------------------------------------------------------------

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

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 generalized 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 (except for family(nbinomial)).

+---------+ ----+ 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 by 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.

asis forces retention of perfect predictor variables and their associated, perfectly predicted observations and may produce instabilities in maximization; see [R] probit. This option is only allowed with option family(binomial) with a denominator of 1.

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.

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

corr(correlation) specifies the within-group 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).

+-----------+ ----+ 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.

vce(robust) specifies that the Huber/White/sandwich estimator of variance is to be used in place of the default conventional variance estimator (see Methods and formulas in [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. Under a noncanonical link, it does, however, require that the model correctly specifies the mean. The resulting standard errors are thus labeled "semirobust" instead of "robust" in this case. 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 is the total number of observations and P is the number 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 errors and confidence intervals as described in maximize. For family(binomial) link(logit) (that is, logistic regression), exponentiation results in odds ratios; for family(poisson) link(log) (that is, Poisson regression), exponentiated coefficients are incidence-rate ratios.

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 the convergence tolerance 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 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 ------------------------------------------- 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 ------------------------------------------- (*) 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.

Stored results

xtgee 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(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) variable denoting time within groups e(model) pa 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) 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(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


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