Stata 15 help for fmm_regress

[FMM] fmm: regress -- Finite mixtures of linear regression models

Syntax

Basic syntax

fmm #: regress depvar [indepvars] [, options]

Full syntax

fmm # [if] [in] [weight] [, fmmopts]: regress depvar [indepvars] [, options]

where # specifies the number of class models.

options Description ------------------------------------------------------------------------- Model noconstant suppress the constant term ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. depvar and indepvars may contain time-series operators; see tsvarlist. For a detailed description of options, see Options in [R] regress.

fmmopts Description ------------------------------------------------------------------------- Model lcinvariant(pclassname) specify parameters that are equal across classes; default is lcinvariant(none) lcprob(varlist) specify independent variables for class probabilities lclabel(name) name of the categorical latent variable; default is lclabel(Class) lcbase(#) base latent class constraints(constraints) apply specified linear constraints collinear keep collinear variables

SE/Robust vce(vcetype) vcetype may be oim, robust, or cluster clustvar

Reporting level(#) set confidence level; default is level(95) nocnsreport do not display constraints noheader do not display header above parameter table nodvheader do not display dependent variables information in the header notable do not display parameter table 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 startvalues(svmethod) method for obtaining starting values; default is startvalues(factor) emopts(maxopts) control EM algorithm for improved starting values noestimate do not fit the model; show starting values instead

coeflegend display legend instead of statistics ------------------------------------------------------------------------- varlist may contain factor variables; see fvvarlist. by, statsby, and svy are allowed; see 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 [FMM] fmm postestimation for features available after estimation. For a detailed description of fmmopts, see Options in [FMM] fmm.

pclassname Description ------------------------------------------------------------------------- cons intercepts and cutpoints coef fixed coefficients errvar covariances of errors scale scaling parameters ------------------------------------------------------------------------- all all the above none none of the above; the default -------------------------------------------------------------------------

Menu

Statistics > FMM (finite mixture models) > Continuous outcomes > Linear regression

Description

fmm: regress fits mixtures of linear regression models; see [FMM] fmm and [R] regress for details.

Remarks

For a general introduction to finite mixture models, see [FMM] fmm intro. For general information about linear regression, see [R] regress.

Examples

--------------------------------------------------------------------------- Setup . webuse stamp

Mixture of three normal distributions of thickness . fmm 3: regress thickness

Estimated probabilities of membership in the three classes . estat lcprob

--------------------------------------------------------------------------- Setup . webuse mus03sub

Mixture of three linear regression models . fmm 3: regress lmedexp income c.age##c.age totchr i.sex

Include totchr as a predictor of class membership . fmm 3, lcprob(totchr): regress lmedexp income c.age##c.age totchr i.sex

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

Stored results

See Stored results in [FMM] fmm.


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