Fits non-crossing regression quantiles as a function of linear covariates and smooth terms via B-splines with difference penalties. Automatic smoothness estimation for several spline terms is allowed.
Package quantregGrowth allows estimation of growth charts via quantile regression. Given a set of percentiles,
estimates non-crossing quantile curves as a flexible function of quantitative covariates (typically age), and possibly
additional linear terms. To ensure flexibility, B-splines with a difference L1 penalty are employed to estimate nonparametrically
the curves; additionally monotonicity and concavity constraints may be also set. Multiple smooth terms are allowed and the amount of smoothness for each term is
efficiently included in the model fitting algorithm, see Muggeo et al. (2020).
plot.gcrq displays the fitted lines along with observations and poitwise confidence intervals.
Vito M.R. Muggeo
Maintainer: Vito M.R. Muggeo <email@example.com>
Muggeo VMR, Torretta F, Eilers PHC, Sciandra M, Attanasio M (2020). Multiple smoothing parameters selection in additive regression quantiles, Statistical Modelling, to appear.
Muggeo VMR, Sciandra M, Tomasello A, Calvo S (2013). Estimating growth charts via nonparametric quantile regression: a practical framework with application in ecology, Environ Ecol Stat, 20, 519-531.
Muggeo VMR (2018). Using the R package quantregGrowth: some examples.
Some references on growth charts (the first two papers employ the so-called LMS method)
Cole TJ, Green P (1992) Smoothing reference centile curves: the LMS method and penalized likelihood. Statistics in Medicine 11, 1305-1319.
Rigby RA, Stasinopoulos DM (2004) Smooth centile curves for skew and kurtotic data modelled using the Box-Cox power exponential distribution. Statistics in Medicine 23, 3053-3076.
Wei Y, Pere A, Koenker R, He X (2006) Quantile regression methods for reference growth charts. Statistics in Medicine 25, 1369-1382.
Some references on regression quantiles
Koenker R (2005) Quantile regression. Cambridge University Press, Cambridge.
Cade BS, Noon BR (2003) A gentle introduction to quantile regression for ecologists. Front Ecol Environ 1, 412-420.
#see ?gcrq for some examples
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