Analysis of interval‐grouped data in weed science: The binnednp Rcpp package

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.

Comparing Non-Thermal Plasma and Cold Stratification: Which Pre-Sowing Treatment Benefits Wild Plant Emergence?

“…When rank tests for NPMLE methods failed to detect significance in cases of intersecting time-to-event curves [ 40 ], an alternative nonparametric approach was employed, involving the calculation of the CDF using the KDE statistic. To enhance bandwidth selection, the asymptotic mean integrated squared error (AMISE) method proposed by Barreiro-Ures et al [ 55 ] was utilized. Subsequently, differences in seedling emergence trends were assessed by calculating the Cramér–von Mises-type distance statistic [ 54 , 55 ].…”

“…To enhance bandwidth selection, the asymptotic mean integrated squared error (AMISE) method proposed by Barreiro-Ures et al [ 55 ] was utilized. Subsequently, differences in seedling emergence trends were assessed by calculating the Cramér–von Mises-type distance statistic [ 54 , 55 ].…”

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“…However, in some instances, such as making a prediction, we might be reluctant to accept a "broken-stick" model to describe a continuous phenomenon and prefer a smooth curve. Another nonparametric option was proposed by Barreiro-Ures et al (2019), which uses a kernel density estimator (KDE) to provide a smooth CDF with no predefined shape:…”

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