[R] tpoisson -- Truncated Poisson regression
Syntax
tpoisson depvar [indepvars] [if] [in] [weight] [, options]
options Description
-------------------------------------------------------------------------
Model
noconstant suppress constant term
ll(#|varname) lower limit for truncation; default is ll(0)
when neither ll() nor ul() is specified
ul(#|varname) upper limit for truncation
exposure(varname_e) include ln(varname_e) in model with
coefficient constrained to 1
offset(varname_o) include varname_o in model with coefficient
constrained to 1
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
SE/Robust
vce(vcetype) vcetype may be oim, robust, cluster clustvar,
opg, bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
irr report incidence-rate ratios
nocnsreport 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
coeflegend display legend instead of statistics
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indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bayes, bootstrap, by, fmm, fp, jackknife, rolling, statsby, and svy are
allowed; see prefix. For more details, see [BAYES] bayes: tpoisson and
[FMM] fmm: tpoisson.
Weights are not allowed with the bootstrap prefix.
vce() and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
coeflegend does not appear in the dialog box.
See [R] tpoisson postestimation for features available after estimation.
Menu
Statistics > Count outcomes > Truncated Poisson regression
Description
tpoisson fits a truncated Poisson regression model when the number of
occurrences of an event is restricted to be above a truncation point,
below a truncation point, or between two truncation points. Truncated
Poisson models are appropriate when neither the dependent variable nor
the covariates are observed in the truncated part of the distribution.
By default, tpoisson assumes left-truncation occurs at zero, but
truncation may be specified at other fixed points or at values that vary
across observations.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
ll(#|varname) and ul(#|varname) specifies the lower and upper limits for
truncation, respectively. You may specify nonnegative integer values
for one or both.
When neither ll() nor ul() is specified, the default is zero
truncation, ll(0), equivalent to left-truncation at zero.
exposure(varname_e), offset(varname_o), 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.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
irr reports estimated 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.
nocnsreport; see [R] estimation options.
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.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, 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 tpoisson but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
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Setup
. webuse runshoes
Truncated Poisson regression with default truncation point of 0
. tpoisson shoes distance i.male age
---------------------------------------------------------------------------
Setup
. replace shoes = . if shoes < 4
Truncated Poisson regression with truncation point of 3 and exposure
variable age
. tpoisson shoes distance male, exposure(age) ll(3)
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Stored results
tpoisson stores the following in e():
Scalars
e(N) number of observations
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) tpoisson
e(cmdline) command as typed
e(depvar) name of dependent variable
e(llopt) contents of ll(), or 0 if neither ll() nor ul() is
specified
e(ulopt) contents of ul(), if specified
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(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
