Growth Charts via Regression Quantiles

Description

Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with difference penalties.

Details

Package quantregGrowth allows estimation of growth charts via quantile regression. Given a set of percentiles, gcrq estimates non-crossing quantile curves as a flexible function of a quantitative covariate (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 constraints may be also set. plot.gcrq displays the fitted lines.

Author(s)

Vito M.R. Muggeo

Maintainer: Vito M.R. Muggeo <vito.muggeo@unipa.it>

References

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.

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

Results