Produces a classic coefficient table (Estimate, Std. Error, z value,
Pr(>|z|)) in the style of summary.glm().
Usage
# S3 method for class 'panglm'
summary(object, ...)Value
an object of class "summary.panglm", a list with components
call (the matched call), model, effect, and family (as stored on
the fit), coefficients (a numeric matrix with one row per parameter
and columns Estimate, Std. Error, z value, Pr(>|z|)), nobs,
n_groups, df.residual, loglik, dispersion (NULL when not
applicable, as for negative binomial fits), iterations, and vcov_type (which covariance estimator
produced the reported standard errors). It has an associated
print.summary.panglm() method and is normally printed rather than
used programmatically. Also included are sigma (random-intercept
standard deviation) and shape (gamma shape) when estimated,
varcomp (covariance matrix of the random intercept and slopes) for
random-slope fits, for family = "negbin" the NB2 negbin_theta
(variance mu + mu^2 / theta) of pooled, fixed-effects and random-slope
fits, with its standard error negbin_theta_se (pooled and
fixed-effects fits, from the observed information at the fitted means,
as in MASS::glm.nb()), or negbin_ab, the beta mixing parameters
a and b of the random-intercept model, and, for
cre = TRUE fits, cre_test: the Wald (Mundlak) test that the
individual-mean coefficients are jointly zero, computed with the
covariance stored on the fit.
See also
Other panglm methods:
coefplot(),
confint.panglm(),
effectcurve(),
fitted.panglm(),
fixef(),
glance.panglm(),
logLik.panglm(),
model.matrix.panglm(),
partialeffects.panordered(),
plot.panglm(),
plot.panglm_ape(),
plot.panglm_effectcurve(),
predict.panglm(),
predict.panordered(),
residuals.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")
summary(fit)
#>
#> Call:
#> panglm(formula = fev1 ~ treatment + age + smoker + crp, data = copd,
#> index = c("id", "visit"), model = "pooling", family = "gaussian")
#>
#> Family: gaussian Link: identity Model: pooling Effect: individual
#> n = 300 groups = 60 vcov: classical
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 4.091479 0.225597 18.136 < 2e-16 ***
#> treatment 0.208556 0.056578 3.686 0.000228 ***
#> age -0.014239 0.003133 -4.545 5.5e-06 ***
#> smoker -0.355207 0.055905 -6.354 2.1e-10 ***
#> crp -0.032895 0.013135 -2.504 0.012269 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Log-likelihood: -189.6
#> Dispersion parameter: 0.2107
#> Residual df: 295 Iterations: 2
#>