Summarize a panqr fit
Usage
# S3 method for class 'panqr'
summary(object, ...)Value
an object of class "summary.panqr": a list with one
coefficient table per quantile (coefficients: estimate, standard
error, z value and p-value), the location and scale tables and the
quantiles q of the standardized errors for MM-QR, and the fit
information (call, model, tau, nobs, n_groups, vcov_type,
R, cluster, n_clusters, dropped).
See also
Other panqr methods:
plot.panqr(),
vcov.panqr()
Examples
set.seed(2)
d <- data.frame(id = rep(1:100, each = 5), t = rep(1:5, 100))
d$x <- rnorm(500) + rep(rnorm(100), each = 5)
d$y <- d$x + rep(rnorm(100), each = 5) + (1 + 0.2 * abs(d$x)) * rnorm(500)
summary(panqr(y ~ x, data = d, index = c("id", "t"), tau = 0.9, model = "mmqr"))
#>
#> Call:
#> panqr(formula = y ~ x, data = d, index = c("id", "t"), tau = 0.9,
#> model = "mmqr")
#>
#> Estimator: MM-QR fixed-effects quantile regression (Machado and Santos Silva 2019)
#> n = 500 groups = 100 vcov: analytic (cluster by individual, G = 100)
#>
#> Location (within regression, cluster-robust):
#> Estimate Std. Error z value Pr(>|z|)
#> x 0.90372 0.05381 16.8 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Scale (within regression, cluster-robust):
#> Estimate Std. Error z value Pr(>|z|)
#> x 0.001334 0.031056 0.043 0.966
#>
#> Quantile tau = 0.9 (q(tau) = 1.685):
#> Estimate Std. Error z value Pr(>|z|)
#> x 0.90597 0.07482 12.11 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>