Implements the generics::glance() generic.
Value
a one-row data.frame including log-likelihood, AIC, BIC, the
likelihood parameter count when a likelihood is available,
R-squared and adjusted R-squared (see Details), and theta, the NB2
dispersion parameter of pooled, fixed-effects and random-slope
negative binomial fits (NA otherwise)
Details
r.squared is 1 - RSS / TSS on the scale of fitted() and
the outcome. For Gaussian fixed-effects fits the fitted values include
the absorbed individual (and, with effect = "twoways", time) effects,
so r.squared is the R-squared of the dummy-variable regression,
fixest::r2(type = "r2"), not the within R-squared reported by
plm. adj.r.squared is 1 - (1 - R^2) (n - 1) / df, with df the
residual degrees of freedom of the fit, which count the absorbed
effects, as fixest::r2(type = "ar2"). For non-Gaussian families both
are Gaussian-style pseudo-R-squared measures, not likelihood based.
See also
tidy-methods for the other model classes.
Other panglm methods:
coefplot(),
confint.panglm(),
effectcurve(),
fitted.panglm(),
fixef(),
logLik.panglm(),
model.matrix.panglm(),
partialeffects.panordered(),
plot.panglm(),
plot.panglm_ape(),
plot.panglm_effectcurve(),
predict.panglm(),
predict.panordered(),
residuals.panglm(),
summary.panglm(),
tidy.panglm(),
vcov.panglm()
Examples
data(copd)
fit <- panglm(fev1 ~ treatment + age + smoker + crp, data = copd,
index = c("id", "visit"), model = "pooling", family = "gaussian")
if (requireNamespace("generics", quietly = TRUE)) {
generics::glance(fit)
}
#> model effect family link vcov.type logLik AIC BIC
#> 1 pooling individual gaussian identity classical -189.5639 391.1278 413.3505
#> df.logLik df.residual r.squared adj.r.squared nobs n.groups theta
#> 1 6 295 0.2417961 0.2315154 300 60 NA