Stata 15 help for fmm_glm

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

Syntax

Basic syntax

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

Full syntax

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

where # specifies the number of class models.

options Description ------------------------------------------------------------------------- Model family(familyname) distribution of depvar; default is family(gaussian) link(linkname) link function; default varies per family noconstant suppress the constant term 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 asis retain perfect predictor variables ------------------------------------------------------------------------- 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] glm.

familyname Description ------------------------------------------------------------------------- gaussian Gaussian (normal); the default bernoulli Bernoulli beta beta binomial [#|varname] binomial; default number of binomial trials is 1 poisson Poisson nbinomial [mean|constant] negative binomial; default dispersion is mean exponential exponential gamma gamma lognormal lognormal loglogistic loglogistic weibull Weibull ------------------------------------------------------------------------- bernoulli, beta, exponential, lognormal, loglogistic, and weibull are extensions available with fmm: glm that are not available with glm.

linkname Description ------------------------------------------------------------------------- identity identity log log logit logit probit probit cloglog complementary log-log -------------------------------------------------------------------------

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) > Generalized linear model (GLM)

Description

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

Remarks

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

Examples

Setup . webuse stamp

Mixture of three lognormal distributions of thickness . fmm 3: glm thickness, family(lognormal)

Estimated probabilities of membership in the three classes . estat lcprob

Stored results

See Stored results in [FMM] fmm.


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