quantregGrowth-package: Non-Crossing Additive Regression Quantiles and Non-Parametric...

In quantregGrowth: Non-Crossing Additive Regression Quantiles and Non-Parametric Growth Charts

Non-Crossing Additive Regression Quantiles and Non-Parametric Growth Charts.

Description

Fits non-crossing regression quantiles as a function of linear covariates and smooth terms via P-splines with difference penalties. Random intercepts and selection of linear variables are allowed via the lasso penalties. Estimation of (possibly adaptive) smoothing/tuning parameters (for the spline terms, the random intercepts and the variables selector) are carried out efficientely as part of model fitting.

Details

Package:	quantregGrowth
Type:	Package
Version:	1.7-1
Date:	2024-05-20
License:	GPL

Package quantregGrowth allows estimation of growth charts via quantile regression. Given a set of percentiles (i.e. probability values), gcrq estimates non-crossing quantile curves as a flexible function of quantitative covariates (typically age in growth charts), and possibly additional linear terms. To ensure flexibility, B-splines with a difference L_1 penalty are employed to estimate non parametrically the curves wherein monotonicity and concavity constraints may be also set. Multiple smooth terms, including varying coefficients, are allowed and the amount of smoothness for each term is efficiently included in the model fitting algorithm, see Muggeo et al. (2021). plot.gcrq displays the fitted lines along with observations and poitwise confidence intervals.

Author(s)

Vito M.R. Muggeo

Maintainer: Vito M.R. Muggeo <[e-mail]>

References

Muggeo VMR, Torretta F, Eilers PHC, Sciandra M, Attanasio M (2021). Multiple smoothing parameters selection in additive regression quantiles, Statistical Modelling, 21, 428-448.

Muggeo VMR (2021). Additive Quantile regression with automatic smoothness selection: the R package quantregGrowth.
https://www.researchgate.net/publication/350844895

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.
https://www.researchgate.net/publication/323573492

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 Also

gcrq, rq in package quantreg

Examples

#see ?gcrq for some examples

quantregGrowth documentation built on May 29, 2024, 6:46 a.m.