Analysis of interval‐grouped data in weed science: The binnednp Rcpp package. Issue 19 (13th September 2019)
- Record Type:
- Journal Article
- Title:
- Analysis of interval‐grouped data in weed science: The binnednp Rcpp package. Issue 19 (13th September 2019)
- Main Title:
- Analysis of interval‐grouped data in weed science: The binnednp Rcpp package
- Authors:
- Barreiro‐Ures, Daniel
Francisco‐Fernández, Mario
Cao, Ricardo
Fraguela, Basilio B.
Doallo, Ramón
González‐Andújar, José Luis
Reyes, Miguel - Abstract:
- Abstract: Weed scientists are usually interested in the study of the distribution and density functions of the random variable that relates weed emergence with environmental indices like the hydrothermal time (HTT). However, in many situations, experimental data are presented in a grouped way and, therefore, the standard nonparametric kernel estimators cannot be computed. Kernel estimators for the density and distribution functions for interval‐grouped data, as well as bootstrap confidence bands for these functions, have been proposed and implemented in the binnednp package. Analysis with different treatments can also be performed using a bootstrap approach and a Cramér‐von Mises type distance. Several bandwidth selection procedures were also implemented. This package also allows to estimate different emergence indices that measure the shape of the data distribution. The values of these indices are useful for the selection of the soil depth at which HTT should be measured which, in turn, would maximize the predictive power of the proposed methods. This paper presents the functions of the package and provides an example using an emergence data set of Avena sterilis (wild oat). The binnednp package provides investigators with a unique set of tools allowing the weed science research community to analyze interval‐grouped data. Abstract : An R package, called binnednp, implementing nonparametric methods for interval‐grouped data is presented. In particular, weed emergence dataAbstract: Weed scientists are usually interested in the study of the distribution and density functions of the random variable that relates weed emergence with environmental indices like the hydrothermal time (HTT). However, in many situations, experimental data are presented in a grouped way and, therefore, the standard nonparametric kernel estimators cannot be computed. Kernel estimators for the density and distribution functions for interval‐grouped data, as well as bootstrap confidence bands for these functions, have been proposed and implemented in the binnednp package. Analysis with different treatments can also be performed using a bootstrap approach and a Cramér‐von Mises type distance. Several bandwidth selection procedures were also implemented. This package also allows to estimate different emergence indices that measure the shape of the data distribution. The values of these indices are useful for the selection of the soil depth at which HTT should be measured which, in turn, would maximize the predictive power of the proposed methods. This paper presents the functions of the package and provides an example using an emergence data set of Avena sterilis (wild oat). The binnednp package provides investigators with a unique set of tools allowing the weed science research community to analyze interval‐grouped data. Abstract : An R package, called binnednp, implementing nonparametric methods for interval‐grouped data is presented. In particular, weed emergence data are usually presented in a grouped way and can be analyzed using this package. An example of emergence data set of Avena sterilis (wild oat) is also included in the paper. … (more)
- Is Part Of:
- Ecology and evolution. Volume 9:Issue 19(2019)
- Journal:
- Ecology and evolution
- Issue:
- Volume 9:Issue 19(2019)
- Issue Display:
- Volume 9, Issue 19 (2019)
- Year:
- 2019
- Volume:
- 9
- Issue:
- 19
- Issue Sort Value:
- 2019-0009-0019-0000
- Page Start:
- 10903
- Page End:
- 10915
- Publication Date:
- 2019-09-13
- Subjects:
- bandwidth selection -- hydrothermal time -- nonparametric kernel estimation -- weed emergence model
Ecology -- Periodicals
Evolution -- Periodicals
577.05 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2045-7758 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/ece3.5448 ↗
- Languages:
- English
- ISSNs:
- 2045-7758
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16243.xml