summary.iqrL {qrcm}

Summary After Quantile Regression Coefficients Modeling With Longitudinal Data

Description

Summary of an object of class “iqrL”.

Usage

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

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.

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.

Author(s)

Paolo Frumento [e-mail]

See Also

iqrL, for model fitting; predict.iqrL and plot.iqrL, for predicting and plotting objects of class “iqrL”; test.fit.iqrL for a goodness-of-fit test.

Examples

# 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