texreg (version 1.22)

extract: Extract coefficients and GOF measures from a statistical object

Description

Extract coefficients and GOF measures from a statistical object.

Usage

extract(model, ...)
extract.clm(model, include.thresholds=TRUE, include.aic=TRUE, include.bic=TRUE, 
    include.loglik=TRUE, include.nobs=TRUE, ...)
extract.clogit(model, include.aic=TRUE, include.rsquared=TRUE, 
    include.maxrs=TRUE, include.events=TRUE, include.nobs=TRUE, 
    include.missings=TRUE, ...)
extract.coxph(model, include.aic=TRUE, include.rsquared=TRUE, 
    include.maxrs=TRUE, include.events=TRUE, include.nobs=TRUE, 
    include.missings=TRUE, include.zph=TRUE, ...)
extract.coxph.penal(model, include.aic=TRUE, include.rsquared=TRUE, 
    include.maxrs=TRUE, include.events=TRUE, include.nobs=TRUE, 
    include.missings=TRUE, include.zph=TRUE, ...)
extract.dynlm(model, include.rsquared=TRUE, include.adjrs=TRUE, 
    include.nobs=TRUE, ...)
extract.ergm(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    ...)
extract.gee(model, robust=TRUE, include.dispersion=TRUE, include.nobs=TRUE, ...)
extract.glm(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    include.deviance=TRUE, include.nobs=TRUE, ...)
extract.glmerMod(model, include.pvalues=FALSE, include.aic=TRUE, 
    include.bic=TRUE, include.loglik=TRUE, include.deviance=TRUE, 
    include.nobs=TRUE, include.groups=TRUE, include.variance=TRUE, 
    mcmc.pvalues=FALSE, mcmc.size=5000, ...)
extract.gls(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    include.nobs=TRUE, ...)
extract.gmm(model, include.obj.fcn=TRUE, include.overidentification=FALSE, 
    include.nobs=TRUE, ...)
extract.lm(model, include.rsquared=TRUE, include.adjrs=TRUE, include.nobs=TRUE, 
    ...)
extract.lme(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    include.nobs=TRUE, ...)
extract.lmerMod(model, include.pvalues=FALSE, include.aic=TRUE, 
    include.bic=TRUE, include.loglik=TRUE, include.deviance=TRUE, 
    include.nobs=TRUE, include.groups=TRUE, include.variance=TRUE, 
    mcmc.pvalues=FALSE, mcmc.size=5000, ...)
extract.lmrob(model, include.nobs=TRUE, ...)
extract.lnam(model, include.rsquared=TRUE, include.adjrs=TRUE, 
    include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, ...)
extract.lrm(model, include.pseudors=TRUE, include.lr=TRUE, include.nobs=TRUE, 
    ...)
extract.mer(model, include.pvalues=FALSE, include.aic=TRUE, 
    include.bic=TRUE, include.loglik=TRUE, include.deviance=TRUE, 
    include.nobs=TRUE, include.groups=TRUE, include.variance=TRUE, 
    mcmc.pvalues=FALSE, mcmc.size=5000, ...)
extract.negbin(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    include.deviance=TRUE, include.nobs=TRUE, ...)
extract.nlmerMod(model, include.pvalues=FALSE, include.aic=TRUE, 
    include.bic=TRUE, include.loglik=TRUE, include.deviance=TRUE, 
    include.nobs=TRUE, include.groups=TRUE, include.variance=TRUE, 
    mcmc.pvalues=FALSE, mcmc.size=5000, ...)
extract.plm(model, include.rsquared=TRUE, include.adjrs=TRUE, 
    include.nobs=TRUE, ...)
extract.pmg(model, include.nobs=TRUE, ...)
extract.polr(model, include.thresholds=FALSE, include.aic=TRUE, 
    include.bic=TRUE, include.loglik=TRUE, include.deviance=TRUE, 
    include.nobs=TRUE, ...)
extract.Relogit(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    include.deviance=TRUE, include.nobs=TRUE, ...)
extract.rem.dyad(model, include.nvertices=TRUE, include.events=TRUE, 
    include.aic=TRUE, include.aicc=TRUE, include.bic=TRUE, ...)
extract.rlm(model, include.nobs=TRUE, ...)
extract.rq(model, include.nobs=TRUE, include.percentile=TRUE, ...)
extract.sclm(model, include.thresholds=TRUE, include.aic=TRUE, include.bic=TRUE,
    include.loglik=TRUE, include.nobs=TRUE, ...)
extract.simex(model, jackknife=TRUE, include.nobs=TRUE, ...)
extract.stergm(model, beside=FALSE, include.formation=TRUE, 
    include.dissolution=TRUE, include.nvertices=TRUE, include.aic=FALSE, 
    include.bic=FALSE, include.loglik=FALSE, ...)
extract.svyglm(model, include.aic=FALSE, include.bic=FALSE, 
    include.loglik=FALSE,include.deviance=TRUE, include.dispersion=TRUE, 
    include.nobs=TRUE, ...)
extract.systemfit(model, include.rsquared=TRUE, include.adjrs=TRUE, 
    include.nobs=TRUE, ...)
extract.tobit(model, include.aic=TRUE, include.bic=TRUE, include.loglik=TRUE, 
    include.deviance=TRUE, include.nobs=FALSE, include.censnobs=TRUE, 
    include.wald=TRUE, ...)

Arguments

model

A statistical model object.

beside

If available: should the model terms be arranged below each other or beside each other (default)? For example, in a stergm model, the formation and dissolution coefficients can be arranged in two different ways.

include.adjrs

If available: should the adjusted R-squared be reported?

include.aic

If available: should Akaike's information criterion (AIC) be reported?

include.aicc

If available: should AICc be reported? This is a version of AIC with a correction for finite sample sizes.

include.bic

If available: should the Bayesian information criterion (BIC) be reported?

include.censnobs

If available: should the total, right-censored, left-censored, and uncensored number of observations be reported?

include.deviance

If available: should the deviance be reported?

include.dispersion

If available: should the dispersion or scale parameter be reported?

include.dissolution

If available: should the coefficients for the dissolution phase in a STERGM be reported?

include.events

If available: should the number of events be reported (in survival models)?

include.formation

If available: should the coefficients for the formation phase in a STERGM be reported?

include.groups

If available: should the number of groups be reported?

include.loglik

If available: should the log-likelihood be reported?

include.lr

If available: should the likelihood ratio test be reported?

include.maxrs

If available: should the maximum possible R-squared be reported?

include.missings

If available: should the number of missing observations be reported (in survival models)?

include.nobs

If available: should the number of observations be reported?

include.nvertices

If available: should the number of vertices be reported in a statistical network model?

include.obj.fcn

If available: should the value of the objective function (= criterion function) be reported (for gmm objects)? More precisely, this returns E(g)var(g)^{-1}E(g).

include.overidentification

If available: should the J-test for overidentification be reported (for gmm objects)?

include.percentile

If available: should the percentile (tau) be reported?

include.pseudors

If available: should the pseudo R-squared be reported?

include.pvalues

If available: should the p-values be reported (not recommended for lme4 models)?

include.rsquared

If available: should R-squared be reported?

include.thresholds

If available: should the threshold parameters (that is, the intercepts for the class boundaries) be reported in ordinal models?

include.variance

If available: should group variances be reported?

include.wald

If available: should the Wald statistic be included?

include.zph

If available: should the Cox proportional hazards assumption be tested (resulting in a p value indicating whether the proportional hazards assumption of the model is violated)?

jackknife

If available: use Jackknife variance instead of Asymptotic variance.

mcmc.pvalues

In linear mixed effects models: compute MCMC-based p-values instead of naive p-values. This will only affect the output if the argument include.pvalues=TRUE is also set. Warning: computing MCMC-based p-values may take some time.

mcmc.size

In linear mixed effects models: the MCMC sample size from which p-values are derived (if the arguments include.pvalues=TRUE and mcmc.pvalues=TRUE are also set). Note: high values may take considerable computing time.

robust

If available: report robust instead of native standard errors.

...

Custom parameters.

Details

extract() is a generic function which extracts coefficients and GOF measures from statistical objects. There are several extract functions for the specific model types, which are called by the generic extract function if it encounters a model known to be handled by the specific function. The output is used by the texreg function.

The various extract functions can also be used directly on a statistical model to convert them into texreg objects.

See Also

texreg-package texreg extract-methods