Worm plot

Worm plot: A simple diagnostic device for modeling growth reference curves. The worm plot visualizes di6erences between two distributions, conditional on the values of a covariate. Though the worm plot is a general diagnostic tool for the analysis of residuals, this paper focuses on an application in constructing growth reference curves, where the covariate of interest is age. The LMS model of Cole and Green is used to construct reference curves in the Fourth Dutch Growth Study 1997. If the model fits, the measurements in the reference sample follow a standard normal distribution on all ages after a suitably chosen Box–Cox transformation. The coe=cients of this transformation are modelled as smooth age-dependent parameter curves for the median, variation and skewness, respectively. The major modelling task is to choose the appropriate amount of smoothness of each parameter curve. The worm plot assesses the age-conditional normality of the transformed data under a variety of LMS models. The fit of each parameter curve is closely related to particular features in the worm plot, namely its o6set, slope and curvature. Application of the worm plot to the Dutch growth data resulted in satisfactory reference curves for a variety of anthropometric measures. It was found that the LMS method generally models the age-conditional mean and skewness better than the age-related deviation and kurtosis. Copyright 2001 John Wiley & Sons, Ltd

References in zbMATH (referenced in 13 articles )

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  1. Prataviera, Fábio; Silva, Antonio M. M.; Cardoso, Elke J. B. N.; Cordeiro, Gauss M.; Ortega, Edwin M. M.: A novel generalized odd log-logistic Maxwell-based regression with application to microbiology (2021)
  2. De Bastiani, Fernanda; Rigby, Robert A.; Stasinopoulous, Dimitrios M.; Cysneiros, Audrey H. M. A.; Uribe-Opazo, Miguel A.: Gaussian Markov random field spatial models in GAMLSS (2018)
  3. Ramires, Thiago G.; Hens, Niel; Cordeiro, Gauss M.; Ortega, Edwin M. M.: Estimating nonlinear effects in the presence of cure fraction using a semi-parametric regression model (2018)
  4. Stasinopoulos, Mikis D.; Rigby, Robert A.; De Bastiani, Fernanda: GAMLSS: a distributional regression approach (2018)
  5. Zhang, Feipeng; Li, Qunhua: A continuous threshold expectile model (2017)
  6. Lakshmi Sujatha, Ch.; Vardhana Rao, M. Vishnu: A new non parametric method of estimation of prediction limits and comparison of its performance with semi parametric method (2014)
  7. Rigby, Ra; Stasinopoulos, Dm; Voudouris, V.: Discussion: a comparison of GAMLSS with quantile regression (2013)
  8. Schnabel, Sabine K.; Eilers, Paul H. C.: Simultaneous estimation of quantile curves using quantile sheets (2013)
  9. Florencio, Lutemberg; Cribari-Neto, Francisco; Ospina, Raydonal: Real estate appraisal of land lots using GAMLSS models (2012)
  10. Voudouris, Vlasios; Gilchrist, Robert; Rigby, Robert; Sedgwick, John; Stasinopoulos, Dimitrios: Modelling skewness and kurtosis with the BCPE density in GAMLSS (2012)
  11. Schnabel, Sabine K.; Eilers, Paul H. C.: Optimal expectile smoothing (2009)
  12. van Buuren, Stef: Worm plot to diagnose fit in quantile regression (2007)
  13. Rigby, Robert A.; Stasinopoulos, D. Mikis: Using the Box-Cox (t) distribution in GAMLSS to model skewness and kurtosis (2006)