help intreg dialogs: intreg svy: intreg
also see: intreg postestimation
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Title
[R] intreg -- Interval regression
Syntax
intreg depvar1 depvar2 [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
het(varlist [, noconstant]) independent variables to model the
variance; use noconstant to suppress
constant term
offset(varname) include varname 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)
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
indepvars and varlist may contain factor variables; see fvvarlist.
depvar1, depvar2, indepvars, and varlist may contain time-series
operators; see tsvarlist.
bootstrap, by, fracpoly, jackknife, mfp, nestreg, rolling, statsby,
stepwise, and svy are allowed; see prefix.
Weights are not allowed with the bootstrap prefix.
aweights are not allowed with the jackknife prefix.
vce() and weights are not allowed with the svy prefix.
aweights, fweights, iweights, and pweights are allowed; see weight.
See [R] intreg postestimation for features available after estimation.
Menu
Statistics > Linear models and related > Censored regression > Interval
regression
Description
intreg fits a model of y=[depvar1, depvar2] on indepvars, where y for
each observation is point data, interval data, left-censored data, or
right-censored data.
depvar1 and depvar2 should have the following form:
type of data depvar1 depvar2
----------------------------------------------
point data a = [a,a] a a
interval data [a,b] a b
left-censored data (-inf,b] . b
right-censored data [a,inf) a .
----------------------------------------------
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
het(varlist[, noconstant]) specifies that varlist be included in the
specification of the conditional variance. This varlist enters the
variance specification collectively as multiplicative
heteroskedasticity.
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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
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 intreg but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
We have a dataset containing wages, truncated and in categories. Some of
the observations on wages are
wage1 wage2
20 25 meaning 20000 <= wages <= 25000
50 . meaning 50000 <= wages
Setup
. webuse intregxmpl
Interval regression
. intreg wage1 wage2 age c.age#c.age nev_mar rural school tenure
Same as above, but suppress constant term
. intreg wage1 wage2 age c.age#c.age nev_mar rural school tenure,
noconstant
Saved results
intreg saves the following in e():
Scalars
e(N) number of observations
e(N_unc) number of uncensored observations
e(N_lc) number of left-censored observations
e(N_rc) number of right-censored observations
e(N_int) number of interval observations
e(k) number of parameters
e(k_aux) number of auxiliary parameters
e(k_eq) number of equations
e(k_eq_model) number of equations in model Wald test
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
e(df_m) model degrees of freedom
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 chi-squared test
e(sigma) sigma
e(se_sigma) standard error of sigma
e(rank) rank of e(V)
e(rank0) rank of e(V) for constant-only model
e(ic) number of iterations
e(rc) return code
e(converged) 1 if converged, 0 otherwise
Macros
e(cmd) intreg
e(cmdline) command as typed
e(depvar) names of dependent variable
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(clustvar) name of cluster variable
e(offset) offset
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(diparm#) display transformed parameter #
e(het) heteroskedasticity, if het() specified
e(ml_score) program used to implement scores
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
e(footnote) program used to implement the footnote display
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
Also see
Manual: [R] intreg
Help: [R] intreg postestimation;
[R] regress, [SVY] svy estimation, [R] tobit, [XT] xtintreg,
[XT] xttobit
