**[R] intreg** -- Interval regression

__Syntax__

**intreg** *depvar1* *depvar2* [*indepvars*] [*if*] [*in*] [*weight*] [**,** *options*]

*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* **.**
missing **.** **.**
----------------------------------------------

*options* Description
-------------------------------------------------------------------------
Model
__nocons__**tant** suppress constant term
__h__**et(***varlist* [**,** __nocons__**tant**]**)** independent variables to model the
variance; use **noconstant** to suppress
constant term
__off__**set(***varname***)** include *varname* in model with
coefficient constrained to 1
__const__**raints(***constraints***)** apply specified linear constraints
__col__**linear** keep collinear variables

SE/Robust
**vce(***vcetype***)** *vcetype* may be **oim**, __r__**obust**, __cl__**uster**
*clustvar*, **opg**, __boot__**strap**, or __jack__**knife**

Reporting
__l__**evel(***#***)** set confidence level; default is
**level(95)**
__nocnsr__**eport** 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

__coefl__**egend** display legend instead of statistics
-------------------------------------------------------------------------
*indepvars* and *varlist* may contain factor variables; see fvvarlist.
*depvar1*, *depvar2*, *indepvars*, and *varlist* may contain time-series
operators; see tsvarlist.
**bayes**, **bootstrap**, **by**, **fmm**, **fp**, **jackknife**, **mfp**, **nestreg**, **rolling**, **statsby**,
**stepwise**, and **svy** are allowed; see prefix. For more details, see
**[BAYES] bayes: intreg** and **[FMM] fmm: intreg**.
Weights are not allowed with the **bootstrap** prefix.
**aweight**s are not allowed with the **jackknife** prefix.
**vce()** and weights are not allowed with the **svy** prefix.
**aweight**s, **fweight**s, **iweight**s, and **pweight**s are allowed; see weight.
**coeflegend** does not appear in the dialog box.
See **[R] intreg postestimation** for features available after estimation.

__Menu__

**Statistics > Linear models and related > Censored regression >** **Interval**
**regression**

__Description__

**intreg** fits a linear model with an outcome measured as point data,
interval data, left-censored data, or right-censored data. As such, it
is a generalization of the models fit by **tobit**.

__Options__

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

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

**het(***varlist*[**, noconstant**]**)** specifies that the logarithm of the standard
deviation be modeled as a linear combination of *varlist*. The
constant is included unless **noconstant** is specified.

**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 (**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**.

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

*display_options*: **noci**, __nopv__**alues**, __noomit__**ted**, **vsquish**, __noempty__**cells**,
__base__**levels**, __allbase__**levels**, __nofvlab__**el**, **fvwrap(***#***)**, **fvwrapon(***style***)**,
**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**. 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**

__Stored results__

**intreg** stores 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 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(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 variables
**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(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(properties)** **b V**
**e(predict)** program used to implement **predict**
**e(marginsok)** predictions allowed 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(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