scholarly journals Measuring the Impact of Nonignorable Missingness Using the R Package isni

2018 ◽  
Vol 164 ◽  
pp. 207-220 ◽  
Author(s):  
Hui Xie ◽  
Weihua Gao ◽  
Baodong Xing ◽  
Daniel F. Heitjan ◽  
Donald Hedeker ◽  
...  
Keyword(s):  
R Package ◽  
Nonignorable Missingness ◽  
The Impact

scholarly journals Impacts of Storm Surges on a Changing Climate: a Global Bibliometric Analysis of the Coastal Zone

2021 ◽  
Author(s):  
Karine Bastos Leal ◽  
Luís Eduardo de Souza Robaina ◽  
André de Souza De Lima
Keyword(s):  
Climate Change ◽  
Bibliometric Analysis ◽  
Coastal Zone ◽  
Storm Surges ◽  
R Package ◽  
The United States ◽  
Average Temperature ◽  
The United Kingdom ◽  
The Impact ◽  
Coastal Research

Abstract An increase in the global mean sea is predicted during the 21st century as a consequence of global average temperature projections. In addition, changes in the strength of atmospheric cyclonic storms may alter the development of storm surges, exacerbating the risks to coastal communities. Based on the fact that the interest and range of papers are growing on this topic, this study aims to present the global scientific production status of studies that have correlated climate change and the impact of storm surges on the coastal zone leading to erosion and flooding (inundation) via a bibliometric analysis. We analyzed 429 papers published in journals between 1991 and February 2021 from the Scopus database. Through the VOSviewer and Bibliometrix R package, we describe the most relevant countries, affiliations, journals, authors, and keywords. Our results demonstrate that there has been an exponential growth in the research topic, and that authors from the United States and the United Kingdom are the most prolific. Among the 1454 authors found, 10 researchers published at least 5 papers on the topic and obtained at least 453 citations in the period. The most represented journals were the Journal of Coastal Research, Climatic Change, and Natural Hazards. We also found, and discuss, the lack of standardization in the choice of keywords, of which climate change, storm surge, and sea level rise are the most frequent. Finally, we have written a guide to facilitate the authors' bibliographic review.

Power Analysis by Simulation using R and simglm

2019 ◽  
Author(s):  
Brandon LeBeau
Keyword(s):  
Repeated Measures ◽  
Worked Examples ◽  
R Package ◽  
Primary Study ◽  
Real World Data ◽  
Power Estimate ◽  
Closed Form Solutions ◽  
Assumption Violations ◽  
Statistical Assumptions ◽  
The Impact

Power is a task that is commonly done prior to collecting data for a primary study. In most cases closed-form solutions are used to estimate power which may statistical assumptions to be able to perform the computations, for example assume residuals are normally distributed. In real-world data, these statistical assumptions may not hold, therefore estimates of power when these assumptions are assumed will likely be inflated. Power by simulation is another way to compute power estimates and offers significant flexibility to the user to explore the impact of various statistical assumption violations may have on power. This tutorial uses the simglm R package to perform the power by simulation. The simglm package provides a framework to simulate data from generalized linear mixed models which includes a wide variety of models. In addition, functions to perform replications and to compute power estimate summaries are available for users to take advantage of. Two worked examples are shown, one for a two-sample t-test and another within a repeated measures or longitudinal framework.

scholarly journals consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction

2019 ◽  
Author(s):  
Ashley J. Waardenberg ◽  
Matt A. Field
Keyword(s):  
Differential Expression ◽  
Simulated Data ◽  
R Package ◽  
Bioconductor Package ◽  
Rna Seq ◽  
User Input ◽  
Extensive Evaluation ◽  
The Impact ◽  
Transcript Database ◽  
Multiple Algorithms

AbstractExtensive evaluation of RNA-seq methods have demonstrated that no single algorithm consistently outperforms all others. Removal of unwanted variation (RUV) has also been proposed as a method for stabilizing differential expression (DE) results. Despite this, it remains a challenge to run multiple RNA-seq algorithms to identify significant differences common to multiple algorithms, whilst also integrating and assessing the impact of RUV into all algorithms. consensusDE was developed to automate the process of identifying significant DE by combining the results from multiple algorithms with minimal user input and with the option to automatically integrate RUV. consensusDE only requires a table describing the sample groups, a directory containing BAM files or preprocessed count tables and an optional transcript database for annotation. It supports merging of technical replicates, paired analyses and outputs a compendium of plots to guide the user in subsequent analyses. Herein, we also assess the ability of RUV to improve DE stability when combined with multiple algorithms through application to real and simulated data. We find that, although RUV demonstrated improved FDR in a setting of low replication, the effect was algorithm specific and diminished with increased replication, reinforcing increased replication for recovery of true DE genes. We finish by offering some rules and considerations for the application of RUV in a consensus-based setting.consensusDE is freely available, implemented in R and available as a Bioconductor package, under the GPL-3 license, along with a comprehensive vignette describing functionality: http://bioconductor.org/packages/consensusDE/

scholarly journals GCalignR: An R package for aligning Gas-Chromatography data

2017 ◽  
Author(s):  
Meinolf Ottensmann ◽  
Martin A. Stoffel ◽  
Hazel J. Nichols ◽  
Joseph I. Hoffman
Keyword(s):  
Gas Chromatography ◽  
Animal Communication ◽  
Kin Recognition ◽  
R Package ◽  
Error Rates ◽  
Fine Tuning ◽  
Alignment Error ◽  
Mass Spectral ◽  
Fur Seals ◽  
The Impact

AbstractChemical cues are arguably the most fundamental means of animal communication and play an important role in mate choice and kin recognition. Consequently, gas chromatography (GC) in combination with either mass spectrometry (MS) or flame ionisation detection (FID) are commonly used to characterise complex chemical samples. Both GC-FID and GC-MS generate chromatograms comprising peaks that are separated according to their retention times and which represent different substances. Across chromatograms of different samples, homologous substances are expected to elute at similar retention times. However, random and often unavoidable experimental variation introduces noise, making the alignment of homologous peaks challenging, particularly with GC-FID data where mass spectral data are lacking. Here we presentGCalignR, a user-friendly R package for aligning GC-FID data based on retention times. The package also implements dynamic visualisations to facilitate inspection and fine-tuning of the resulting alignments, and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses. We demonstrate an example workflow using empirical data from Antarctic fur seals and explore the impact of user-defined parameter values by calculating alignment error rates for multiple datasets. The resulting alignments had low error rates for most of the explored parameter space and we could also show thatGCalignRperformed equally well or better than other available software. We hope thatGCalignRwill help to simplify the processing of chemical datasets and improve the standardization and reproducibility of chemical analyses in studies of animal chemical communication and related fields.

scholarly journals GeoChronR – an R package to model, analyze and visualize age-uncertain paleoscientific data

2020 ◽  
Author(s):  
Nicholas P. McKay ◽  
Julien Emile-Geay ◽  
Deborah Khider
Keyword(s):  
Open Source ◽  
Real World ◽  
Principal Component ◽  
R Package ◽  
Model Uncertainties ◽  
Data Standards ◽  
Spectral Analyses ◽  
Modeling Techniques ◽  
Age Model ◽  
The Impact

Abstract. Chronological uncertainty is a hallmark of the paleosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here we describe GeoChronR, an open-source R package to facilitate these tasks. GeoChronR is built around emerging data standards for the paleosciences (Linked PaleoData, or LiPD), and offers access to four popular age modeling techniques (Bacon, BChron, Oxcal, BAM). The output of these models is used to support ensemble-aware analyses, quantifying the impact of chronological uncertainties on common analyses like age-uncertain correlation, regression, principal component, and spectral analyses. We present five real-world use cases to illustrate how GeoChronR may be used to facilitate these tasks, to visualize the results in intuitive ways, and to store the results for further analysis, promoting transparency and reusability.

scholarly journals Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (4) ◽  
pp. e1003585
Author(s):  
Kyra H. Grantz ◽  
Elizabeth C. Lee ◽  
Lucy D’Agostino McGowan ◽  
Kyu Han Lee ◽  
C. Jessica E. Metcalf ◽  
...  
Keyword(s):  
Control Strategies ◽  
R Package ◽  
Sensitivity Analyses ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Case Detection ◽  
Modeling Framework ◽  
Online Application ◽  
Almost All ◽  
The Impact

Background Test-trace-isolate programs are an essential part of Coronavirus Disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods and findings We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. Conclusions Effective test-trace-isolate programs first need to be strong in the “test” component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.

scholarly journals PIC v1.3: comprehensive R package for computing permafrost indices with daily weather observations and atmospheric forcing over the Qinghai–Tibet Plateau

2018 ◽  
Vol 11 (6) ◽  
pp. 2475-2491 ◽  
Author(s):  
Lihui Luo ◽  
Zhongqiong Zhang ◽  
Wei Ma ◽  
Shuhua Yi ◽  
Yanli Zhuang
Keyword(s):  
Ground Surface ◽  
R Package ◽  
Frozen Soil ◽  
Tibet Plateau ◽  
Trend Test ◽  
Active Layer Thickness ◽  
Permafrost Degradation ◽  
Spatial Trends ◽  
The Impact ◽  
Qinghai Tibet Plateau

Abstract. An R package was developed for computing permafrost indices (PIC v1.3) that integrates meteorological observations, gridded meteorological datasets, soil databases, and field measurements to compute the factors or indices of permafrost and seasonal frozen soil. At present, 16 temperature- and depth-related indices are integrated into the PIC v1.3 R package to estimate the possible trends of frozen soil in the Qinghai–Tibet Plateau (QTP). These indices include the mean annual air temperature (MAAT), mean annual ground surface temperature (MAGST), mean annual ground temperature (MAGT), seasonal thawing–freezing n factor (nt∕nf), thawing–freezing degree-days for air and the ground surface (DDTa∕DDTs∕DDFa∕DDFs), temperature at the top of the permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freeze depth. PIC v1.3 supports two computational modes, namely the stations and regional calculations that enable statistical analysis and intuitive visualization of the time series and spatial simulations. Datasets of 52 weather stations and a central region of the QTP were prepared and simulated to evaluate the temporal–spatial trends of permafrost with the climate. More than 10 statistical methods and a sequential Mann–Kendall trend test were adopted to evaluate these indices in stations, and spatial methods were adopted to assess the spatial trends. Multiple visual methods were used to display the temporal and spatial variability of the stations and region. Simulation results show extensive permafrost degradation in the QTP, and the temporal–spatial trends of the permafrost conditions in the QTP are close to those of previous studies. The transparency and repeatability of the PIC v1.3 package and its data can be used and extended to assess the impact of climate change on permafrost.

Model selection in statistical historical biogeography of Neotropical insects—theExophthalmusgenus complex (Curculionidae: Entiminae)

2016 ◽  
Author(s):  
Guanyang Zhang ◽  
Usmaan Basharat ◽  
Nicholas Matzke ◽  
Nico M. Franz
Keyword(s):  
Model Selection ◽  
R Package ◽  
Molecular Dating ◽  
Fossil Calibration ◽  
Caribbean Islands ◽  
Biogeographic History ◽  
Founder Event ◽  
Caribbean Biogeography ◽  
The Impact

ABSTRACTStatistical historical biogeographical methods rely on the use of models that assume various biogeographic processes. Until recently model selection remains an explored topic and the impacts of using different models on inferring biogeographic history are poorly understood. Focusing on the Neotropical weevils in theExophthalmusgenus complex (Insecta: Curculionidae: Entiminae), we compare three commonly used biogeographic models – DIVA (Dispersal-Vicariance Analysis), DEC (Dispersal-Extinction-Cladogenesis) and BayArea (Bayesian Analysis of Biogeography), and examine the impact of modeling founder-event jump dispersal on biogeographic history estimation. We also investigate the biogeographic events that have shaped patterns of distributions, diversification, and endemism in this group of weevils. We sample representatives of 65 species of theExophthalmusgenus complex and 26 outgroup terminals from the Neotropics including Caribbean islands and mainland. We reconstruct a molecular phylogeny based on six genes and performed molecular dating using a relaxed clock with three fossil calibration points. We conduct biogeographic history estimations and compare alternative biogeographic models with the R package BioGeoBEARS. Model selection strongly favors biogeographic models that include founder-event jump dispersal. Without modeling jump dispersal, estimations based on the three biogeographic models are dramatically different, especially at early diverging nodes. When jump dispersal is modeled, however, the three biogeographic models perform similarly. Accordingly, we show that the Neotropical mainland was colonized by Caribbean species in the early Miocene, and thatin situdiversification accounts for a majority (~75%) of the biogeographic events in theExophthalmusgenus complex. Our study highlights the need for testing for wide-ranging historical biogeographic processes in the study of Caribbean biogeography and the importance of comparing and selecting the best-fitting model in statistical biogeographic inferences. We demonstrate that modeling founder-event jump dispersal significantly improves the fit of the biogeographic history estimation of Caribbean and Neotropical mainland weevils. We establish thatin situdiversification acts as a dominant biogeographic force in the evolution of theExophthalmusgenus complex. The colonization of the Neotropical mainland from Caribbean islands reinforces the notion that islands can be an important source of continental diversity.

The various facets of digital repeat photography fully exploited with the phenopix R package

2020 ◽  
Author(s):  
Gianluca Filippa ◽  
Edoardo Cremonese ◽  
Marta Galvagno ◽  
Mirco Migliavacca
Keyword(s):  
Land Surface ◽  
Gross Primary Production ◽  
Rapid Development ◽  
Vegetation Indices ◽  
Region Of Interest ◽  
Climate Extremes ◽  
R Package ◽  
Seasonal Development ◽  
Special Focus ◽  
The Impact

&lt;p&gt;Flux towers are more and more often equipped with digital cameras (aka phenocams) widely used to track canopy greenness. Phenocam-derived vegetation indices can capture land surface phenology but also seasonality in gross primary production (GPP) estimated from eddy covariance (EC) measurements. In addition, phenocams can be used to track seasonal development of different species or individuals within the same image scene, and evaluate spatial variability within the footprint of EC measurements. Further, phenocams were recently used to quantify disturbance such as late frost, fires, storms etc. in forested ecosystems and the impact of climate extremes on ecosystem functioning. With the recent rapid development of phenocameras, the need for up-to-date, efficient, open-source software is also increasing tremendously. The phenopix R package was developed for this purpose. In this contribution, we will provide an overview of the software capabilities, with a special focus on how EC measurements can benefit from phenocam data streams.&lt;/p&gt;&lt;p&gt;The steps of a basic processing workflow will be illustrated, including drawing a region of interest (ROI) on an image; extracting red, green and blue digital numbers from a seasonal series of images; depicting greenness index trajectories; fitting a curve to the seasonal trajectories; extracting relevant phenological thresholds (phenophases); characterizing phenophase uncertainties. A focus will be made on recent software developments, including the calculation of camera-derived NDVI and other infrared-based indices, and the handling of shifts in the field of view of the phenocameras.&lt;/p&gt;

scholarly journals Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Nils Arrigo ◽  
Jarek W Tuszynski ◽  
Dorothee Ehrich ◽  
Tommy Gerdes ◽  
Nadir Alvarez
Keyword(s):  
Genetic Variation ◽  
R Package ◽  
The Impact
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