betar_family | R Documentation |
Family objects for distributional regression with the (extended-support) beta distribution via bamlss or gamlss2.
betar_family(link = "logit", link.phi = "log", ...)
xbetax_family(link = "logit", link.phi = "log", link.nu = "log",
quad = 20, tol = .Machine$double.eps^0.7, ...)
link |
character specification of the link function in
the mean model (mu). Currently, |
link.phi |
character specification of the link function in
the precision model (phi). Currently, |
link.nu |
character specification of the link function in
the exceedence model (nu). Currently, |
quad |
numeric. The number of quadrature points for numeric
integration of the continuous mixture in |
tol |
numeric. Accuracy (convergence tolerance) for numerically
determining quantiles based on |
... |
Arguments passed to functions that are called within the family object. |
Family objects for bamlss (Umlauf et al. 2019, 2021) and gamlss2
(Umlauf et al. 2024) are essentially lists of functions providing a standardized
interface to the d/p/q/r functions of distributions. Hence, betar_family
interfaces the classical beta distribution in regression specification, see
dbetar
. Analogously, xbetax_family
interfaces the
extended-support beta mixture specification (Kosmidis and Zeileis 2024),
see dxbetax
.
A list of class family.bamlss
.
Kosmidis I, Zeileis A (2024). Extended-Support Beta Regression for [0, 1] Responses. 2409.07233, arXiv.org E-Print Archive. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2409.07233")}
Umlauf N, Klein N, Zeileis A (2019). BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond). Journal of Computational and Graphical Statistics, 27(3), 612–627. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2017.1407325")}
Umlauf N, Klein N, Simon T, Zeileis A (2021). bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond). Journal of Statistical Software, 100(4), 1–53. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v100.i04")}
Umlauf N, Stasinopoulos M, Rigby R, Stauffer R (2024). gamlss2: Infrastructure for Flexible Distributional Regression. R package version 0.1-0. https://gamlss-dev.github.io/gamlss2/
dbetar
, dxbetax
, family.bamlss
## package and data
library("betareg")
library("bamlss")
data("ReadingSkills", package = "betareg")
## classical beta regression via ML
rs1 <- betareg(accuracy ~ dyslexia * iq | dyslexia + iq, data = ReadingSkills)
## IGNORE_RDIFF_BEGIN
## Bayesian additive model (with low number of iterations to speed up the example)
set.seed(0)
rs2 <- bamlss(accuracy ~ s(iq, by = dyslexia) | dyslexia + iq, data = ReadingSkills,
family = betar_family(), eps = 1e-7, n.iter = 400, burnin = 100)
## Bayesian model shrinks the effects compared to ML
plot(accuracy ~ iq, data = ReadingSkills, pch = 19, col = dyslexia)
nd <- data.frame(
iq = rep(-19:20/10, 2),
dyslexia = factor(rep(c("no", "yes"), each = 40), levels = c("no", "yes"))
)
nd$betareg <- predict(rs1, newdata = nd, type = "response")
nd$bamlss <- predict(rs2, newdata = nd, type = "parameter", model = "mu")
lines(betareg ~ iq, data = nd, subset = dyslexia == "no", col = 1, lwd = 2, lty = 1)
lines(betareg ~ iq, data = nd, subset = dyslexia == "yes", col = 2, lwd = 2, lty = 1)
lines(bamlss ~ iq, data = nd, subset = dyslexia == "no", col = 1, lwd = 2, lty = 2)
lines(bamlss ~ iq, data = nd, subset = dyslexia == "yes", col = 2, lwd = 2, lty = 2)
legend("topleft", c("Dyslexia: no", "Dyslexia: yes", "betareg", "bamlss"),
lty = c(0, 0, 1, 2), pch = c(19, 19, NA, NA), col = c(1, 2, 1, 1), bty = "n")
## IGNORE_RDIFF_END
## xbetax_family(): requires more time due to Gaussian quadrature
## for gamlss2: install.packages("gamlss2", repos = "https://gamlss-dev.R-universe.dev")
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