performance: An R Package for Assessment, Comparison and Testing of Statistical Models

Lüdecke, Daniel; Ben‐Shachar, Mattan S.; Patil, Indrajeet; Waggoner, Philip; Makowski, Dominique

doi:10.31234/osf.io/vtq8f

Cited by 195 publications

(179 citation statements)

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“…R (version 3.6.3) was used for statistical analyses and scientific graphics [33]. The following R packages were included: "agricolae" (non-parametric Kruskal-Wallis test) [34], "lme4" and "lmerTest" (mixed models) [26,35], "ggplot2" (graphs) [36], and "performance" (r-squared values) [37].…”

Section: Discussionmentioning

confidence: 99%

The Method and Timing of Weed Control Affect the Productivity of Intercropped Maize (Zea mays L.) and Bean (Phaseolus vulgaris L.)

Andert

2021

Agriculture

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Intercropping results in high overall system productivity on a given piece of land due to the efficient use of available plant growth resources. Weed control in intercropping systems is, however, still a challenge. This study focuses on the effect of the type of weed control (chemical/mechanical) and the timing of chemical herbicide application (pre-emergence/post-emergence) on weed control and crop productivity in a maize bean mixture. For this purpose, the results of a three-year field experiment (2017 to 2019) in northeast Germany will be presented. The experimental setup included a control, three chemical methods, and one mechanical method of weed control. Except for the mechanical treatment, a completely randomized block design was established. Weeds were assessed at BBCH 12 of the maize, immediately before the first weed control treatments to estimate the initial weed infestation, and twice in the six to eight leaf stage of the maize. The weed coverage (%) was estimated. The maize bean mixture was harvested and yields (t ha−1) were measured. The results confirm that the type of weed control, as well as the timing of herbicide application, significantly affects the weed coverage of the maize bean mixture. The most successful weed control strategy was the double chemical herbicide application, in which chemical herbicides are in used pre-emergence beans (BBCH 12 maize) and post-emergence beans (BBCH 12 bean). Weed coverage was reduced by up to 75% using this most effective herbicide strategy and by up to 61% through mechanical weed control, compared to the control. The additional effects of post-emergence treatment on the pre-emergence herbicide control resulted in a 16.5% weed coverage decline. The yield surplus of double chemical herbicide application (pre/post-emergence) was up to 53%, and for the mechanical weed control up to 23%. Additionally, post-emergence herbicide use in intercropped maize and bean resulted in a 16% yield surplus, compared to the single pre-emergence chemical weed control. Optimal timing of weed control during the most sensitive phenology stages of the maize bean mixture is crucial for productivity. The results of this study provide an additional option for suitable weed control of intercropped maize and bean.

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Section: Discussionmentioning

confidence: 99%

The Method and Timing of Weed Control Affect the Productivity of Intercropped Maize (Zea mays L.) and Bean (Phaseolus vulgaris L.)

Andert

2021

Agriculture

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“…We took a similar approach to model the relationship between predictors and the dependent variable proportion-on-target reads with modifications to account for the dependent variable being a proportion. In particular, we used the R package glmmTMB ( Brooks et al, 2017 ) in order to utilize the beta error distribution appropriate for a proportional response variable, scaled predictors prior to modeling using the R package dplyr ( Wickham et al, 2020 ), performed model selection using the dredge function in the R package MuMIn ( Barton, 2020 ), checked for collinearity using the check_collinearity function in the R package performance ( Lüdecke et al, 2020 ), and calculated pseudo- R 2 -values for the top model using the r2_nakagawa function in the R package performance ( Lüdecke et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success

et al. 2021

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Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality (“greenness”) as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.

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“…We conducted analyses in R 33 using RStudio 34 with the following statistical packages: broom.mixed , 36 lubridate , 37 magrittr , 38 readxl , 39 see , 40 sf , 41 tidyverse , 42 tmap , 43 and zoo 44 . Aggregate data and analytical code are archived on a public repository 45 .…”

Section: Methodsmentioning

confidence: 99%

Geographic variation in work disability burden: Identifying hotspots, coldspots, and associated sociodemographic factors

Lane

Collie

2021

American J Industrial Med

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Objectives To identify geographic hotspots and coldspots of work disability burden and their associated sociodemographic factors in Australia. Methods Using Australian workers' compensation and census data, we calculated weeks of compensated time off work per 1000 labor force, an indicator of work disability burden, at Statistical Area Level 4, the smallest level of labor force data produced by the Australian Bureau of Statistics. Records included all claims with at least 1 day of compensated time off work lodged between 2010 and 2015. Work disability burden was z‐transformed by state and territory and mapped across Australia. Statistical Areas that were more than 1 standard deviation from the state or territory mean were considered hotspots and coldspots. We tested several sociodemographic factors as predictors of work disability burden. Results Work disability burden hotspots were concentrated in lower socioeconomic suburbs and exurbs of state capitals, in addition to several regional areas. Coldspots were primarily in wealthy central urban and suburban areas. Factors associated with greater work disability burden include higher area socioeconomic disadvantage, rurality, lower labor force participation, higher unemployment, higher proportion of work in production industries and in blue‐collar occupations, and higher numbers of those with core activity limitations, those aged 65+ years, and foreign‐born residents. Conclusions Work disability burden is unequally distributed across Australia and strongly influenced by sociodemographic and occupational factors. The findings can guide more efficient allocation of resources for work disability prevention and rehabilitation.

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performance: An R Package for Assessment, Comparison and Testing of Statistical Models

Cited by 195 publications

References 0 publications

The Method and Timing of Weed Control Affect the Productivity of Intercropped Maize (Zea mays L.) and Bean (Phaseolus vulgaris L.)

The Method and Timing of Weed Control Affect the Productivity of Intercropped Maize (Zea mays L.) and Bean (Phaseolus vulgaris L.)

The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success

Geographic variation in work disability burden: Identifying hotspots, coldspots, and associated sociodemographic factors

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