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‘Qest’ implements the quantile-based estimators (Q-estimators) of Sottile and Frumento (2022): any parametric model is fitted through its quantile function by minimizing the integrated loss of quantile regression. Q-estimators are usually more robust than maximum likelihood, and allow for censored and truncated data.
Qest(): user-defined quantile functions, or the
families Qnorm, Qgamma, Qpois
(continuous Poisson) and Qunif;Qlm(): linear models with normal errors;Qcoxph(): proportional hazards models with a piecewise
linear baseline hazard.install.packages("Qest")
# development version
remotes::install_github("gianluca-sottile/Qest")library(Qest)
set.seed(1)
n <- 200
dat <- data.frame(x = runif(n))
dat$y <- rgamma(n, shape = 2, scale = exp(1 + dat$x))
Qg <- function(theta, tau, data) {
qgamma(tau, shape = exp(theta[1]), scale = exp(theta[2] + theta[3] * data$x))
}
m1 <- Qest(y ~ x, Q = Qg, start = c(NA, NA, NA), data = dat)
m2 <- Qest(y ~ x, Q = Qgamma, data = dat)
cbind(user = coef(m1), family = coef(m2))
#> user family
#> Theta1 0.6636152 0.6635124
#> Theta2 0.9955430 0.9956836
#> Theta3 0.9858171 0.9858114
summary(m2)
#>
#> Call: Qest(formula = y ~ x, Q = Qgamma, data = dat)
#>
#> n. of observations: 200
#> n. of parameters: 3
#> n. of iterations: 4
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> log(shape) 0.6635 0.1084 6.120 9.37e-10 ***
#> (Intercept) 0.9957 0.1650 6.034 1.59e-09 ***
#> x 0.9858 0.1933 5.099 3.41e-07 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Minimized loss function: 331.7161
#> Sottile, G. and Frumento, P. (2022). Robust estimation and regression with parametric quantile functions. Computational Statistics and Data Analysis, 171, 107471. https://doi.org/10.1016/j.csda.2022.107471
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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