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.
Highlights
The knowledge of the factors affecting the emergence patterns of weeds is interesting from a plant ecology perspective, and in applied research, where the emergence of weeds is an import‐ ant phase of the population dynamics (González‐Andújar, 2008)
Several bandwidth selection procedures were implemented. This package 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 hydrothermal time (HTT) should be measured which, in turn, would maximize the predictive power of the proposed methods
Temperature and water potential have been identified as essential factors that control weed emergence (Forcella et al, 2000)
Summary
The knowledge of the factors affecting the emergence patterns of weeds is interesting from a plant ecology perspective, and in applied research, where the emergence of weeds is an import‐ ant phase of the population dynamics (González‐Andújar, 2008). The problem of studying the relation between HTT and weed emergence has been dealt with through nonparametric esti‐ mation of the distribution and density functions of cumulative HTT (CHTT) at emergence (Cao, Francisco‐Fernández, Anand, Bastida, & González‐Andújar, 2013; Reyes, Francisco‐Fernández, & Cao, 2016) These nonparametric methods have been recently proven to outper‐ form the usual regression approaches in terms of prediction error (González‐Andújar, Francisco‐Fernández, et al, 2016). When gathering experimental data, a different prob‐ lem arises due to the fact that seedlings are generally buried at dif‐ ferent depths and, the best depth at which HTT should be measured has to be selected For this task, emergence indices have been defined and nonparametric estimators for them have been constructed (Cao, Francisco‐Fernández, Anand, Bastida, & González‐Andújar, 2011). The techniques required for both, the nonparametric estimation of the density and distribution functions and the emergence indices, have been implemented in the binnednp Rcpp package (Barreiro et al, 2019)
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