summary.iqrL: Summary After Quantile Regression Coefficients Modeling With Longitudinal Data

Description

Summary of an object of class “iqrL”.

Usage

# S3 method for iqrL
summary(object, p, level, cov = FALSE, ...)

Value

If p is supplied, a standard summary of the estimated quantile regression coefficients is returned for each value of p: if level = 1, a summary of beta(p), and if level = 2, a summary of gamma(p). If cov = TRUE, the covariance matrix is also reported.

If p is missing (the default), a list with the following items:

converged: logical value indicating the convergence status.
n.it: the number of iterations.
n: the number of observations.
n.id: the number of unique ids.
free.par: the number of free parameters in the model, excluding fixed effects.
theta: the estimate of \(\theta\).
se.theta: the estimated standard errors associated with theta.
phi: the estimate of \(\phi\).
se.phi: the estimated standard errors associated with phi.
test.row.theta, test.row.phi: Wald test for the covariates. Each row of theta and phi is tested for nullity.
test.col.theta, test.col.phi: Wald test for the building blocks of the quantile function. Each column of theta and phi is tested for nullity.
obj.function: the minimized loss function.
call: the matched call.

Arguments

object: an object of class “iqrL”, the result of a call to iqrL.
p: an optional vector of quantiles.
level: a numeric scalar. Use level = 1 to summarize \(\beta(u)\), and level = 2 to summarize \(\gamma(v)\). Ignored if p is missing.
cov: logical. If TRUE, the covariance matrix of the coefficients or is reported. Ignored if p is missing.
...: for future methods.

Author

Paolo Frumento paolo.frumento@unipi.it

Examples

Run this code


# using simulated data

n <- 1000 # n. of observations
n.id <- 100 # n. of clusters
id <- rep(1:n.id, each = n/n.id) # cluster id

x <- rexp(n) # a covariate

V <- runif(n.id) # V_i
U <- runif(n) # U_it
y <- 1 + 2*log(U) + 3*x + 0.5*qnorm(V)

# true quantile function: Q(u,v | x) = beta0(u) + beta1(u)*x + gamma0(v), with
   # beta0(u) = 1 + 2*log(u)
   # beta1(u) = 3
   # gamma0(v) = 0.5*qnorm(v)

model <- iqrL(fx = y ~ x, fu = ~ 1 + I(log(u)), fz = ~ 1, fv = ~ -1 + I(qnorm(v)), id = id)
summary(model)
summary(model, level = 1, p = c(0.25, 0.75)) # summary of beta(u) at selected quantiles
summary(model, level = 2, p = c(0.1, 0.9)) # summary of gamma(v) at selected quantiles