**xtmepoisson** has been renamed to **meqrpoisson**. **xtmepoisson** continues to
work but, as of Stata 13, is no longer an official part of Stata. This
is the original help file, which we will no longer update, so some links
may no longer work.

-------------------------------------------------------------------------------

__Title__

**[XT] xtmepoisson** -- Multilevel mixed-effects Poisson regression

__Syntax__

**xtmepoisson** *depvar* [*fe_equation*] **||** *re_equation* [**||** *re_equation* ...]
[**,** *options*]

where the syntax of *fe_equation* is

[*indepvars*] [*if*] [*in*] [**,** *fe_options*]

and the syntax of *re_equation* is one of the following:

for random coefficients and intercepts

*levelvar***:** [*varlist*] [**,** *re_options*]

for a random effect among the values of a factor variable

*levelvar***:** **R.***varname* [**,** *re_options*]

*levelvar* is a variable identifying the group structure for the random
effects at that level or **_all** representing one group comprising all
observations.

*fe_options* Description
-------------------------------------------------------------------------
Model
__noc__**onstant** suppress constant term from the
fixed-effects equation
__exp__**osure(***varname_e***)** include ln(*varname_e*) in model with
coefficient constrained to 1
__off__**set(***varname_o***)** include *varname_o* in model with
coefficient constrained to 1
-------------------------------------------------------------------------

*re_options* Description
-------------------------------------------------------------------------
Model
__cov__**ariance(***vartype***)** variance-covariance structure of the
random effects
__noc__**onstant** suppress the constant from the
random-effects equation
__col__**linear** keep collinear variables
-------------------------------------------------------------------------

*options* Description
-------------------------------------------------------------------------
Integration
__lap__**lace** use Laplacian approximation; equivalent
to **intpoints(1)**
__intp__**oints(***#* [*#* ...]**)** set the number of integration
(quadrature) points; default is 7

Reporting
__l__**evel(***#***)** set confidence level; default is
**level(95)**
__ir__**r** report fixed-effects coefficients as
incidence-rate ratios
__var__**iance** show random-effects parameter estimates
as variances and covariances
__noret__**able** suppress random-effects table
__nofet__**able** suppress fixed-effects table
__estm__**etric** show parameter estimates in the
estimation metric
__nohead__**er** suppress output header
__nogr__**oup** suppress table summarizing groups
__nolr__**test** do not perform LR test comparing to
Poisson regression
*display_options* control column formats, row spacing, and
display of omitted variables and base
and empty cells

Maximization
*maximize_options* control the maximization process during
gradient-based optimization; seldom
used
__retol__**erance(***#***)** tolerance for random-effects estimates;
default is **retolerance(1e-8)**; seldom
used
__reiter__**ate(***#***)** maximum number of iterations for
random-effects estimation; default is
**reiterate(50)**; seldom used
**matsqrt** parameterize variance components using
matrix square roots; the default
**matlog** parameterize variance components using
matrix logarithms
__refine__**opts(***maximize_options***)** control the maximization process during
refinement of starting values

__coefl__**egend** display legend instead of statistics
-------------------------------------------------------------------------

*vartype* Description
-------------------------------------------------------------------------
__ind__**ependent** one variance parameter per random effect, all
covariances zero; the default unless a factor
variable is specified
__ex__**changeable** equal variances for random effects, and one
common pairwise covariance
__id__**entity** equal variances for random effects, all
covariances zero; the default if factor
variables are specified
__un__**structured** all variances-covariances distinctly estimated
-------------------------------------------------------------------------

*indepvars* may contain factor variables; see fvvarlist.
*indepvars* and *varlist* may contain time-series operators; see tsvarlist.
**bootstrap**, **by**, **jackknife**, **mi estimate**, **rolling**, and **statsby** are allowed;
see prefix.
**coeflegend** does not appear in the dialog box.
See **[XT] xtmepoisson postestimation** for features available after
estimation.

__Menu__

**Statistics > Longitudinal/panel data > Multilevel mixed-effects models >**
**Mixed-effects Poisson regression**

__Description__

**xtmepoisson** fits mixed-effects models for count responses. Mixed models
contain both fixed effects and random effects. The fixed effects are
analogous to standard regression coefficients and are estimated directly.
The random effects are not directly estimated (although they may be
obtained postestimation) but are summarized according to their estimated
variances and covariances. Random effects may take the form of either
random intercepts or random coefficients, and the grouping structure of
the data may consist of multiple levels of nested groups. The
distribution of the random effects is assumed to be Gaussian. The
conditional distribution of the response given the random effects is
assumed to be Poisson. Because the log likelihood for this model has no
closed form, it is approximated by adaptive Gaussian quadrature.

__Options__

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

**noconstant** suppresses the constant (intercept) term and may be specified
for the fixed effects equation and for any or all the random-effects
equations.

**exposure(***varname_e***)** specifies a variable that reflects the amount of
exposure over which the *depvar* events were observed for each
observation; ln(*varname_e*) is included in the fixed-effects portion
of the model with coefficient constrained to be 1.

**offset(***varname_o***)** specifies that *varname_o* be included in the
fixed-effects portion of the model with the coefficient constrained
to be 1.

**covariance(***vartype***)**, where *vartype* is

**independent**|**exchangeable**|**identity**|**unstructured**

specifies the structure of the covariance matrix for the random
effects and may be specified for each random-effects equation. An
**independent** covariance structure allows a distinct variance for each
random effect within a random-effects equation and assumes that all
covariances are zero. **exchangeable** covariances have common variances
and one common pairwise covariance. **identity** is short for "multiple
of the identity"; that is, all variances are equal and all
covariances are zero. **unstructured** allows for all variances and
covariances to be distinct. If an equation consists of *p*
random-effects terms, the **unstructured** covariance matrix will have
*p*(*p*+1)/2 unique parameters.

**covariance(independent)** is the default, except when the
random-effects equation consists of the factor-variable specification
**R.***varname*, in which case **covariance(identity)** is the default, and
only **covariance(identity)** and **covariance(exchangeable)** are allowed.

**collinear** specifies that **xtmepoisson** not omit collinear variables from
the random-effects equation. Usually there is no reason to leave
collinear variables in place, and in fact doing so usually causes the
estimation to fail because of the matrix singularity caused by the
collinearity. However, with certain models (for example, a
random-effects model with a full set of contrasts), the variables may
be collinear, yet the model is fully identified because of
restrictions on the random-effects covariance structure. In such
cases, using the **collinear** option allows the estimation to take place
with the random-effects equation intact.

+-------------+
----+ Integration +------------------------------------------------------

**laplace** specifies that log likelihoods be calculated using the Laplacian
approximation, equivalent to adaptive Gaussian quadrature with one
integration point for each level in the model; **laplace** is equivalent
to **intpoints(1)**. Computation time increases as a function of the
number of quadrature points raised to a power equaling the dimension
of the random-effects specification. The computational time saved by
using **laplace** can thus be substantial, especially when you have many
levels and/or random coefficients.

The Laplacian approximation has been known to produce biased
parameter estimates, but the bias tends to be more prominent in the
estimates of the variance components rather than in estimates of the
fixed effects. If your interest lies primarily with the
fixed-effects estimates, the Laplace approximation may be a viable
faster alternative to adaptive quadrature with multiple integration
points.

Specifying a factor variable, **R.***varname*, increases the dimension of
the random effects by the number of distinct values of *varname*, that
is, the number of factor levels. Even when this number is small to
moderate, it increases the total random-effects dimension to the
point where estimation with more than one quadrature point is
prohibitively intensive.

For this reason, when you have factor variables in your
random-effects equations, the **laplace** option is assumed. You can
override this behavior by using the **intpoints()** option, but doing so
is not recommended.

**intpoints(***# *[*# *...]**)** sets the number of integration points for adaptive
Gaussian quadrature. The more points, the more accurate the
approximation to the log likelihood. However, computation time
increases with the number of quadrature points, and in models with
many levels and/or many random coefficients, this increase can be
substantial.

You may specify one number of integration points applying to all
levels of random effects in the model, or you may specify distinct
numbers of points for each level. **intpoints(7)** is the default; that
is, by default seven quadrature points are used for each level.

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

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

**irr** reports the fixed-effects coefficients transformed to incidence-rate
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. **irr** may be
specified at estimation or when replaying previously estimated
results.

**variance** displays the random-effects parameter estimates as variances and
covariances. The default is to display them as standard deviations
and correlations.

**noretable** suppresses the random-effects table from the output.

**nofetable** suppresses the fixed-effects table from the output.

**estmetric** displays all parameter estimates in the estimation metric.
Fixed-effects estimates are unchanged from those normally displayed,
but random-effects parameter estimates are displayed as log-standard
deviations and hyperbolic arctangents of correlations, with equation
names that organize them by level.

**noheader** suppresses the output header, either at estimation or upon
replay.

**nogroup** suppresses the display of group summary information (number of
groups, average group size, minimum, and maximum) from the output
header.

**nolrtest** prevents **xtmepoisson** from performing a likelihood-ratio test
that compares the mixed-effects Poisson model with standard
(marginal) Poisson regression. This option may also be specified
upon replay to suppress this test from the output.

*display_options*: __noomit__**ted**, **vsquish**, __noempty__**cells**, __base__**levels**,
__allbase__**levels**, **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**. Those that require special
mention for **xtmepoisson** are listed below.

For the **technique()** option, the default is **technique(nr)**. The **bhhh**
algorithm may not be specified.

**from(***init_specs***)** is particularly useful when combined with
**refineopts(iterate(0))**, which bypasses the initial optimization
stage; see below.

**retolerance(***#***)** specifies the convergence tolerance for the estimated
random effects used by adaptive Gaussian quadrature. Gaussian
quadrature points are adapted to be centered at the estimated random
effects given a current set of model parameters. Estimating these
random effects is an iterative procedure, with convergence declared
when the maximum relative change in the random effects is less than
**retolerance()**. The default **retolerance()** is 1e-8. You should seldom
have to use this option.

**reiterate(***#***)** specifies the maximum number of iterations used when
estimating the random effects to be used in adapting the Gaussian
quadrature points; see the **retolerance()** option. The default is
**reiterate(50)**. You should seldom have to use this option.

**matsqrt** (the default), during optimization, parameterizes variance
components by using the matrix square roots of the
variance-covariance matrices formed by these components at each model
level.

**matlog**, during optimization, parameterizes variance components by using
the matrix logarithms of the variance-covariance matrices formed by
these components at each model level.

The **matsqrt** parameterization ensures that variance-covariance
matrices are positive semidefinite, while **matlog** ensures matrices
that are positive definite. For most problems, the matrix square
root is more stable near the boundary of the parameter space.
However, if convergence is problematic, one option may be to try the
alternate **matlog** parameterization. When convergence is not an issue,
both parameterizations yield equivalent results.

**refineopts(***maximize_options***)** controls the maximization process during the
refinement of starting values. Estimation in **xtmepoisson** takes place
in two stages. In the first stage, starting values are refined by
holding the quadrature points fixed between iterations. During the
second stage, quadrature points are adapted with each evaluation of
the log likelihood. Maximization options specified within
**refineopts()** control the first stage of optimization; that is, they
control the refining of starting values.

*maximize_options* specified outside **refineopts()** control the second
stage.

The one exception to the above rule is the **nolog** option, which when
specified outside **refineopts()** applies globally.

**from(***init_specs***)** is not allowed within **refineopts()** and instead must
be specified globally.

Refining starting values helps make the iterations of the second
stage (those that lead toward the solution) more numerically stable.
In this regard, of particular interest is **refineopts(iterate(***#***))**,
with two iterations being the default. Should the maximization fail
because of instability in the Hessian calculations, one possible
solution may be to increase the number of iterations here.

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

**coeflegend**; see **[R] estimation options**.

__Remarks on specifying random-effects equations__

Mixed models consist of fixed effects and random effects. The fixed
effects are specified as regression parameters in a manner similar to
that of most other Stata estimation commands, that is, as a dependent
variable followed by a set of regressors. The random-effects portion of
the model is specified by first considering the grouping structure of the
data. For example, if random effects are to vary according to variable
**school**, then the call to **xtmepoisson** would be of the form

**. xtmepoisson** *fixed_portion* **|| school:** ... **,** *options*

The variable lists that make up each equation describe how the random
effects enter into the model, either as random intercepts (constant term)
or as random coefficients on regressors in the data. One may also
specify the variance-covariance structure of the within-equation random
effects, according to the four available structures described above. For
example,

**. xtmepoisson** *f_p* **|| school: z1, covariance(unstructured)** *options*

will fit a model with a random intercept and random slope for variable **z1**
and treat the variance-covariance structure of these two random effects
as unstructured.

If the data are organized by a series of nested groups, for example,
classes within schools, then the random-effects structure is specified by
a series of equations, each separated by **||**. The order of nesting
proceeds from left to right. For our example, this would mean that an
equation for schools would be specified first, followed by an equation
for classes. As an example, consider

**. xtmepoisson** *f_p* **|| school: z1, cov(un) || class: z2 z3, nocons**
**cov(ex)** *options*

where variables **school** and **class** identify the schools and classes within
schools, respectively. This model contains a random intercept and random
coefficient on **z1** at the school level and has random coefficients on
variables **z2** and **z3** at the class level. The covariance structure for the
random effects at the class level is exchangeable, meaning that the
random effects share a common variance, and they are allowed to be
correlated. A simplification allowing for no correlation (while still
allowing a common variance) would be **cov(identity)**.

Group variables may be repeated, allowing for more general covariance
structures to be constructed as block-diagonal matrices based on the four
original structures. Consider

**. xtmepoisson** *f_p* **|| school: z1 z2, nocons cov(id) || school: z3**
**z4, nocons cov(un)** *options*

which specifies four random coefficients at the school level. The
variance-covariance matrix of the random effects is the 4 x 4 matrix
where the upper 2 x 2 diagonal block is a multiple of the identity matrix
and the lower 2 x 2 diagonal block is unstructured. In effect, the
coefficients on **z1** and **z2** are constrained to be independent and share a
common variance. The coefficients on **z3** and **z4** each have a distinct
variance and a variance distinct from that of the coefficients on **z1** and
**z2**. They are also allowed to be correlated, yet they are independent
from the coefficients on **z1** and **z2**.

For mixed models with no nested grouping structure, thinking of the
entire estimation data as one group is convenient. Toward this end,
**xtmepoisson** allows the special group designation **_all**. **xtmepoisson** also
allows the factor variable notation **R.***varname*, which is shorthand for
describing the levels of *varname* as a series of indicator variables. See
example 5 in **[XT] xtmelogit** for more details.

__Examples__

---------------------------------------------------------------------------
Setup
**. webuse epilepsy**

Two-level random-intercept model, analogous to **xtpoisson**
**. xtmepoisson seizures treat lbas lbas_trt lage v4 || subject:**

Two-level random-intercept and random-coefficient model
**. xtmepoisson seizures treat lbas lbas_trt lage visit || subject:**
**visit**

Two-level random-intercept and random-coefficient model, correlated
random effects
**. xtmepoisson seizures treat lbas lbas_trt lage visit || subject:**
**visit, cov(unstructured) intpoints(9)**

Replay results with incidence-rate ratios and variances/covariances
**. xtmepoisson, irr variance**

---------------------------------------------------------------------------
Setup
**. webuse melanoma**

Three-level nested model, observations nested **region** nested within **nation**
**. xtmepoisson deaths uv c.uv#c.uv, exposure(expected) || nation: ||**
**region:**

Four-level nested model, fit using **laplace**
**. xtmepoisson deaths uv c.uv#c.uv, exposure(expected) || nation: ||**
**region: || county:, laplace**

---------------------------------------------------------------------------

__Saved results__

**xtmepoisson** saves the following in **e()**:

Scalars
**e(N)** number of observations
**e(k)** number of parameters
**e(k_f)** number of FE parameters
**e(k_r)** number of RE parameters
**e(k_rs)** number of standard deviations
**e(k_rc)** number of correlations
**e(df_m)** fixed-effects model degrees of freedom
**e(ll)** log likelihood
**e(chi2)** chi-squared
**e(p)** p-value for chi-squared
**e(ll_c)** log likelihood, comparison model
**e(chi2_c)** chi-squared, comparison model
**e(df_c)** degrees of freedom, comparison model
**e(p_c)** p-value, comparison model
**e(rank)** rank of **e(V)**
**e(reparm_rc)** return code, final reparameterization
**e(rc)** return code
**e(converged)** **1** if converged, **0** otherwise

Macros
**e(cmd)** **xtmepoisson**
**e(cmdline)** command as typed
**e(depvar)** name of dependent variable
**e(ivars)** grouping variables
**e(exposurevar)** exposure variable
**e(model)** **Poisson**
**e(title)** title in estimation output
**e(offset)** linear offset variable
**e(redim)** random-effects dimensions
**e(vartypes)** variance-structure types
**e(revars)** random-effects covariates
**e(n_quad)** number of integration points
**e(laplace)** **laplace**, if Laplace approximation
**e(chi2type)** **Wald**; type of model chi-squared test
**e(vce)** **bootstrap** or **jackknife** if defined
**e(vcetype)** title used to label Std. Err.
**e(method)** **ML**
**e(opt)** type of optimization
**e(ml_method)** type of **ml** method
**e(technique)** maximization technique
**e(datasignature)** the checksum
**e(datasignaturevars)** variables used in calculation of checksum
**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(Cns)** constraints matrix
**e(N_g)** group counts
**e(g_min)** group-size minimums
**e(g_avg)** group-size averages
**e(g_max)** group-size maximums
**e(V)** variance-covariance matrix of the estimators

Functions
**e(sample)** marks estimation sample