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texreg (version 1.35)

extract: Extract coefficients and GOF measures from a statistical object

Description

Extract coefficients and GOF measures from a statistical object.

Usage

extract(model, ...)
## S3 method for class 'aftreg':
extract(model, include.loglik = TRUE, 
    include.lr = TRUE, include.nobs = TRUE, include.events = TRUE, 
    include.trisk = TRUE, ...)
## S3 method for class 'Arima':
extract(model, include.pvalues = FALSE, 
    include.aic = TRUE, include.loglik = TRUE, ...)
## S3 method for class 'averaging':
extract(model, use.ci = FALSE, 
    adjusted.se = FALSE, include.nobs = TRUE, ...)
## S3 method for class 'betareg':
extract(model, include.precision = TRUE, 
    include.pseudors = TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'brglm':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'btergm':
extract(model, level = 0.95, ...)
## S3 method for class 'censReg':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'clm':
extract(model, include.thresholds = TRUE, 
    include.aic = TRUE, include.bic=TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'clogit':
extract(model, include.aic = TRUE, 
    include.rsquared = TRUE, include.maxrs = TRUE, 
    include.events = TRUE, include.nobs = TRUE, 
    include.missings = TRUE, ...)
## S3 method for class 'coeftest':
extract(model, ...)
## S3 method for class 'coxph':
extract(model, include.aic = TRUE, 
    include.rsquared = TRUE, include.maxrs=TRUE, 
    include.events = TRUE, include.nobs = TRUE, 
    include.missings = TRUE, include.zph = TRUE, ...)
## S3 method for class 'coxph.penal':
extract(model, include.aic = TRUE, 
    include.rsquared = TRUE, include.maxrs = TRUE, 
    include.events = TRUE, include.nobs = TRUE, 
    include.missings = TRUE, include.zph = TRUE, ...)
## S3 method for class 'dynlm':
extract(model, include.rsquared = TRUE, 
    include.adjrs = TRUE, include.nobs = TRUE, 
    include.fstatistic = FALSE, include.rmse = TRUE, ...)
## S3 method for class 'ergm':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, ...)
## S3 method for class 'ergmm':
extract(model, include.bic = TRUE, ...)
## S3 method for class 'fGARCH':
extract(model, include.nobs = TRUE, 
    include.aic = TRUE, include.loglik = TRUE, ...)
## S3 method for class 'gam':
extract(model, include.smooth = TRUE, 
    include.aic = TRUE, include.bic = TRUE, 
    include.loglik = TRUE, include.deviance = TRUE, 
    include.dev.expl = TRUE, include.dispersion = TRUE, 
    include.rsquared = TRUE, include.gcv = TRUE, 
    include.nobs = TRUE, include.nsmooth = TRUE, ...)
## S3 method for class 'gamlss':
extract(model, robust = FALSE, 
    include.nobs = TRUE, include.nagelkerke = TRUE, 
    include.gaic = TRUE, ...)
## S3 method for class 'gee':
extract(model, robust = TRUE, 
    include.dispersion = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'glm':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'glmerMod':
extract(model, method = c("naive", 
    "profile", "boot", "Wald"), level = 0.95, nsim = 1000, 
    include.aic = TRUE, include.bic = TRUE, include.dic = FALSE, 
    include.deviance = FALSE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = TRUE, ...)
## S3 method for class 'glmmadmb':
extract(model, include.variance = TRUE, 
    include.dispersion = TRUE, include.zero = TRUE, 
    include.aic = TRUE, include.bic = TRUE, 
    include.loglik = TRUE, include.nobs = TRUE, 
    include.groups = TRUE, ...)
## S3 method for class 'glmrob':
extract(model, include.nobs = TRUE, ...)
## S3 method for class 'gls':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'gmm':
extract(model, include.obj.fcn = TRUE, 
    include.overidentification = FALSE, include.nobs = TRUE, ...)
## S3 method for class 'hurdle':
extract(model, beside = FALSE, 
    include.count = TRUE, include.zero = TRUE, include.aic = TRUE, 
    include.loglik = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'ivreg':
extract(model, include.rsquared = TRUE, 
    include.adjrs = TRUE, include.nobs = TRUE, 
    include.fstatistic = FALSE, include.rmse = TRUE, ...)
## S3 method for class 'lm':
extract(model, include.rsquared = TRUE, 
    include.adjrs = TRUE, include.nobs = TRUE, 
    include.fstatistic = FALSE, include.rmse = TRUE, ...)
## S3 method for class 'lme':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = FALSE, ...)
## S3 method for class 'lme4':
extract(model, method = c("naive", 
    "profile", "boot", "Wald"), level = 0.95, nsim = 1000, 
    include.aic = TRUE, include.bic = TRUE, include.dic = FALSE, 
    include.deviance = FALSE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = TRUE, ...)
## S3 method for class 'lmerMod':
extract(model, method = c("naive", 
    "profile", "boot", "Wald"), level = 0.95, nsim = 1000, 
    include.aic = TRUE, include.bic = TRUE, include.dic = FALSE, 
    include.deviance = FALSE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = TRUE, ...)
## S3 method for class 'lmrob':
extract(model, include.nobs = TRUE, ...)
## S3 method for class 'lnam':
extract(model, include.rsquared = TRUE, 
    include.adjrs = TRUE, include.aic = TRUE, include.bic = TRUE, 
    include.loglik = TRUE, ...)
## S3 method for class 'lrm':
extract(model, include.pseudors = TRUE, 
    include.lr = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'maBina':
extract(model, ...)
## S3 method for class 'mer':
extract(model, method = c("naive", 
    "profile", "boot", "Wald"), level = 0.95, nsim = 1000, 
    include.aic = TRUE, include.bic = TRUE, include.dic = FALSE, 
    include.deviance = FALSE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = TRUE, ...)
## S3 method for class 'mnlogit':
extract(model, include.aic = TRUE, 
    include.loglik = TRUE, include.nobs = TRUE, 
    include.groups = TRUE, include.intercept = TRUE, 
    include.iterations = FALSE, beside = FALSE, ...)
## S3 method for class 'mlogit':
extract(model, include.aic = TRUE, 
    include.loglik = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'model.selection':
extract(model, include.loglik = TRUE, 
    include.aicc = TRUE, include.delta = TRUE, 
    include.weight = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'multinom':
extract(model, include.pvalues = TRUE, 
    include.aic = TRUE, include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'negbin':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'netlogit':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.deviance = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'nlme':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = FALSE, ...)
## S3 method for class 'nlmerMod':
extract(model, method = c("naive", 
    "profile", "boot", "Wald"), level = 0.95, nsim = 1000, 
    include.aic = TRUE, include.bic = TRUE, include.dic = FALSE, 
    include.deviance = FALSE, include.loglik = TRUE, 
    include.nobs = TRUE, include.groups = TRUE, 
    include.variance = TRUE, ...)
## S3 method for class 'ols':
extract(model, include.nobs = TRUE, 
    include.rsquared = TRUE, include.adjrs = TRUE, 
    include.fstatistic = FALSE, include.lr = TRUE, ...)
## S3 method for class 'pgmm':
extract(model, include.nobs = TRUE, 
    include.sargan = TRUE, include.wald = TRUE, ...)
## S3 method for class 'phreg':
extract(model, include.loglik = TRUE, 
    include.lr = TRUE, include.nobs = TRUE, include.events = TRUE, 
    include.trisk = TRUE, ...)
## S3 method for class 'plm':
extract(model, include.rsquared = TRUE, 
    include.adjrs = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'pmg':
extract(model, include.nobs = TRUE, ...)
## S3 method for class 'polr':
extract(model, include.thresholds = FALSE, 
    include.aic = TRUE, include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'rem.dyad':
extract(model, include.nvertices = TRUE, 
    include.events = TRUE, include.aic = TRUE, 
    include.aicc = TRUE, include.bic = TRUE, ...)
## S3 method for class 'rlm':
extract(model, include.nobs = TRUE, ...)
## S3 method for class 'rq':
extract(model, include.nobs = TRUE, 
    include.percentile = TRUE, ...)
## S3 method for class 'sarlm':
extract(model, include.nobs = TRUE, 
    include.lambda = TRUE, include.aic = TRUE, 
    include.loglik = TRUE, include.wald = TRUE, ...)
## S3 method for class 'sclm':
extract(model, include.thresholds = TRUE, 
    include.aic = TRUE, include.bic = TRUE, include.loglik = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'selection':
extract(model, prefix = TRUE, 
    include.selection = TRUE, include.outcome = TRUE, 
    include.errors = TRUE, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.rsquared = TRUE, include.adjrs = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'sienaFit':
extract(model, include.iterations = TRUE, 
    ...)
## S3 method for class 'simex':
extract(model, jackknife = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'stergm':
extract(model, beside = FALSE, 
    include.formation = TRUE, include.dissolution = TRUE, 
    include.nvertices = TRUE, include.aic = FALSE, 
    include.bic = FALSE, include.loglik = FALSE, ...)
## S3 method for class 'survreg':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'survreg.penal':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'svyglm':
extract(model, include.aic = FALSE, 
    include.bic = FALSE, include.loglik = FALSE, 
    include.deviance = TRUE, include.dispersion = TRUE, 
    include.nobs = TRUE, ...)
## S3 method for class 'systemfit':
extract(model, include.rsquared = TRUE, 
    include.adjrs = TRUE, include.nobs = TRUE, ...)
## S3 method for class 'texreg':
extract(model, ...)
## S3 method for class 'tobit':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = FALSE, 
    include.censnobs = TRUE, include.wald=TRUE, ...)
## S3 method for class 'weibreg':
extract(model, include.loglik = TRUE, 
    include.lr = TRUE, include.nobs = TRUE, 
    include.events = TRUE, include.trisk = TRUE, ...)
## S3 method for class 'zelig':
extract(model, include.aic = TRUE, 
    include.bic = TRUE, include.loglik = TRUE, 
    include.deviance = TRUE, include.nobs = TRUE, 
    include.rsquared = TRUE, include.adjrs = TRUE, 
    include.fstatistic = TRUE, ...)
## S3 method for class 'zeroinfl':
extract(model, beside = FALSE, 
    include.count = TRUE, include.zero = TRUE, include.aic = TRUE, 
    include.loglik = TRUE, include.nobs = TRUE, ...)

Arguments

model
A statistical model object.
adjusted.se
If available: use adjusted rather than default standard errors?
beside
If available: should the model terms be arranged below each other or beside each other? For example, in a stergm model, the formation and dissolution coefficients can be arranged in two columns of the table.
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.count
If available: should the count model of a zero-inflated or hurdle regression be included in the coefficients block (before the zero-inflation or zero hurdle model)?
include.delta
If available: should the delta statistic be included?
include.dev.expl
If available: should the deviance explained be reported?
include.deviance
If available: should the deviance be reported?
include.dic
If available: should the deviance information criterion (DIC) 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.errors
If available: should the error terms of a sample selection model 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.fstatistic
If available: should the F statistic be reported?
include.gaic
If available: should the Generalized Akaike's information criterion (GAIC) be reported?
include.gcv
If available: should the GCV score be reported (in GAMs)?
include.groups
If available: should the number of groups in a mixed-effects model (or k alternatives in a multinomial choice model) be reported?
include.intercept
If available: should the intercept be included in the GOF block?
include.iterations
If available: should the number of iterations be included?
include.lambda
If available: should the lambda statistic and p-value 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.nagelkerke
If available: should Nagelkerke's R-squared be reported?
include.nobs
If available: should the number of observations be reported?
include.nsmooth
If available: should the number of smooth terms be reported (in GAMs)?
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.outcome
If available: should the outcome component of a sample selection model be reported?
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.precision
If available: should the precision estimates of a betareg fit (the phi coefficients) be reported as part of the coefficients block?
include.pseudors
If available: should the pseudo R-squared be reported?
include.pvalues
If available: should the p values be reported (naive p values are not recommended for lme4 models, but see also the mcmc.pvalues argument)?
include.rmse
If available: should the root-mean-square error (= residual standard deviation) be reported?
include.rsquared
If available: should R-squared be reported?
include.sargan
If available: should the Sargan test be reported?
include.selection
If available: should the selection component of a sample selection model be reported?
include.smooth
If available: should the smooth terms of a GAM be reported? If they are reported, the EDF value is reported as the coefficient, and DF is included in parentheses (not standard errors because a chi-square test is used for the smooth terms).
include.thresholds
If available: should the threshold parameters (that is, the intercepts for the class boundaries) be reported in ordinal models?
include.trisk
If available: should the total time at risk be reported (in event-history models)?
include.variance
If available: should group variances be reported?
include.wald
If available: should the Wald statistic be included?
include.weight
If available: should the weight be included?
include.zero
If available: should the zero-inflation model of a zero-inflated regression or the zero hurdle model of a hurdle regression be included in the coefficients block (after the count model)?
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.
level
Confidence level (1 - alpha) for computing confidence intervals.
method
The method used to compute confidence intervals or p values. In lme4 models, the default value "naive" computes naive p values while the other methods compute confidence intervals using the confint function.
nsim
In linear mixed effects models: the MCMC sample size or number of bootstrapping replications on the basis of which confidence intervals are computed (only if the method argument does not specify "naive", which is the default beha
prefix
Include prefix before the label of the coefficient in order to identify the current model component.
robust
If available: report robust instead of naive standard errors.
use.ci
Use confidence intervals rather than standard errors.
...
Custom parameters which are handed over to subroutines.

Details

extract is a generic function which extracts coefficients and GOF measures from statistical model objects. There are several extract methods 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 method. The output is a texreg object, which is subsequently used by the texreg function.

References

Leifeld, Philip (2013). texreg: Conversion of Statistical Model Output in R to LaTeX and HTML Tables. Journal of Statistical Software, 55(8), 1-24. http://www.jstatsoft.org/v55/i08/.

See Also

texreg-package texreg