scholarly journals drda: An R package for dose-response data analysis

2021 ◽  
Author(s):  
Alina Malyutina ◽  
Jing Tang ◽  
Alberto Pessia
Keyword(s):  
Least Squares ◽  
Dose Response ◽  
Large Scale ◽  
Goodness Of Fit ◽  
Trust Region ◽  
R Package ◽  
Response Data ◽  
Goodness Of Fit Tests ◽  
Starting Point ◽  
User Friendly

Analysis of dose-response data is an important step in many scientific disciplines, including but not limited to pharmacology, toxicology, and epidemiology. The R package drda is designed to facilitate the analysis of dose-response data by implementing efficient and accurate functions with a familiar interface. With drda, it is possible to fit models by the method of least squares, perform goodness of fit tests, and conduct model selection. Compared to other similar packages, drda provides, in general, more accurate estimates in the least-squares sense. This result is achieved by a smart choice of the starting point in the optimization algorithm and by implementing the Newton method with a trust region with analytical gradients and Hessian matrices. In this article, drda is presented through the description of its methodological components and examples of its user-friendly functions. Performance is finally evaluated using a real, large-scale drug sensitivity screening dataset.

scholarly journals speciesgeocodeR: An R package for linking species occurrences, user-defined regions and phylogenetic trees for biogeography, ecology and evolution

2015 ◽  
Author(s):  
Alexander Zizka ◽  
Alexandre Antonelli
Keyword(s):  
Data Quality ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Data Cleaning ◽  
R Package ◽  
Species Occurrence ◽  
Occurrence Data ◽  
User Friendly ◽  
Species Occurrences

1. Large-scale species occurrence data from geo-referenced observations and collected specimens are crucial for analyses in ecology, evolution and biogeography. Despite the rapidly growing availability of such data, their use in evolutionary analyses is often hampered by tedious manual classification of point occurrences into operational areas, leading to a lack of reproducibility and concerns regarding data quality. 2. Here we present speciesgeocodeR, a user-friendly R-package for data cleaning, data exploration and data visualization of species point occurrences using discrete operational areas, and linking them to analyses invoking phylogenetic trees. 3. The three core functions of the package are 1) automated and reproducible data cleaning, 2) rapid and reproducible classification of point occurrences into discrete operational areas in an adequate format for subsequent biogeographic analyses, and 3) a comprehensive summary and visualization of species distributions to explore large datasets and ensure data quality. In addition, speciesgeocodeR facilitates the access and analysis of publicly available species occurrence data, widely used operational areas and elevation ranges. Other functionalities include the implementation of minimum occurrence thresholds and the visualization of coexistence patterns and range sizes. SpeciesgeocodeR accompanies a richly illustrated and easy-to-follow tutorial and help functions.

scholarly journals BioRssay: An R Package for Analyses of Bioassays and Probit Graphs

2021 ◽  
Author(s):  
Piyal Karunarathne ◽  
Nicolas Pocquet ◽  
Pierrick Labbé ◽  
Pascal Milesi
Keyword(s):  
Public Health ◽  
Dose Response ◽  
Software Package ◽  
R Package ◽  
World Health ◽  
Research Areas ◽  
Exposure Response ◽  
The World ◽  
Health Organization ◽  
User Friendly

Abstract Dose-response relationships reflect the effects of a substance on organisms, and are widely used in broad research areas, from medicine and physiology, to vector control and pest management in agronomy. Furthermore, reporting on the response of organisms to stressors is an essential component of many public policies (e.g. public health, environment), and assessment of xenobiotic responses is an integral part of the World Health Organization recommendations. Building upon an R script that we previously made available, and considering its popularity, we have now developed a software package in the R environment, BioRssay, to efficiently analyze dose-response relationships. It has more user-friendly functions, more flexibility, and proposes an easy interpretation of the results. The functions in the BioRssay package are built on robust statistical analyses to compare the dose/exposure-response of various bioassays and effectively visualize them in probit-graphs.

scholarly journals Methods for meta-analysis of pharmacodynamic dose–response data with application to multi-arm studies of alogliptin

2016 ◽  
Vol 27 (2) ◽  
pp. 564-578 ◽  
Author(s):  
Oliver Langford ◽  
Jeffrey K Aronson ◽  
Gert van Valkenhoef ◽  
Richard J Stevens
Keyword(s):  
Randomized Controlled Trials ◽  
Least Squares ◽  
Dose Response ◽  
Meta Analysis ◽  
Weighted Least Squares ◽  
Controlled Trials ◽  
Response Model ◽  
Response Data ◽  
Randomized Controlled ◽  
The Mean

Standard methods for meta-analysis of dose–response data in epidemiology assume a model with a single scalar parameter, such as log-linear relationships between exposure and outcome; such models are implicitly unbounded. In contrast, in pharmacology, multi-parameter models, such as the widely used Emax model, are used to describe relationships that are bounded above and below. We propose methods for estimating the parameters of a dose–response model by meta-analysis of summary data from the results of randomized controlled trials of a drug, in which each trial uses multiple doses of the drug of interest (possibly including dose 0 or placebo). We assume that, for each randomized arm of each trial, the mean and standard error of a continuous response measure and the corresponding allocated dose are available. We consider weighted least squares fitting of the model to the mean and dose pairs from all arms of all studies, and a two-stage procedure in which scalar inverse-variance meta-analysis is performed at each dose, and the dose–response model is fitted to the results by weighted least squares. We then compare these with two further methods inspired by network meta-analysis that fit the model to the contrasts between doses. We illustrate the methods by estimating the parameters of the Emax model to a collection of multi-arm, multiple-dose, randomized controlled trials of alogliptin, a drug for the management of diabetes mellitus, and further examine the properties of the four methods with sensitivity analyses and a simulation study. We find that all four methods produce broadly comparable point estimates for the parameters of most interest, but a single-stage method based on contrasts between doses produces the most appropriate confidence intervals. Although simpler methods may have pragmatic advantages, such as the use of standard software for scalar meta-analysis, more sophisticated methods are nevertheless preferable for their advantages in estimation.

scholarly journals Mercator: a pipeline for multi-method, unsupervised visualization and distance generation

2021 ◽  
Author(s):  
Zachary B Abrams ◽  
Caitlin E Coombes ◽  
Suli Li ◽  
Kevin R Coombes
Keyword(s):  
Large Scale ◽  
R Package ◽  
High Dimensional ◽  
Vast Number ◽  
Large Scale Data ◽  
User Friendly ◽  
Exploratory Pattern ◽  
Scale Data ◽  
Selection Of ◽  
Publication Quality

Abstract Summary Unsupervised machine learning provides tools for researchers to uncover latent patterns in large-scale data, based on calculated distances between observations. Methods to visualize high-dimensional data based on these distances can elucidate subtypes and interactions within multi-dimensional and high-throughput data. However, researchers can select from a vast number of distance metrics and visualizations, each with their own strengths and weaknesses. The Mercator R package facilitates selection of a biologically meaningful distance from 10 metrics, together appropriate for binary, categorical and continuous data, and visualization with 5 standard and high-dimensional graphics tools. Mercator provides a user-friendly pipeline for informaticians or biologists to perform unsupervised analyses, from exploratory pattern recognition to production of publication-quality graphics. Availabilityand implementation Mercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html).

scholarly journals R2ucare: An R package to perform goodness-of-fit tests for capture-recapture models

2017 ◽  
Author(s):  
Olivier Gimenez ◽  
Jean-Dominique Lebreton ◽  
Remi Choquet ◽  
Roger Pradel
Keyword(s):  
Statistical Model ◽  
Goodness Of Fit ◽  
Real Data ◽  
R Package ◽  
Goodness Of Fit Tests ◽  
State Capture ◽  
Capture Recapture ◽  
Quality Of Fit

Assessing the quality of fit of a statistical model to data is a necessary step for conducting safe inference. We introduce R2ucare, an R package to perform goodness-of-fit tests for open single- and multi-state capture-recapture models. R2ucare also has various functions to manipulate capture-recapture data. We remind the basics and provide guidelines to navigate towards testing the fit of capture-recapture models. We demonstrate the functionality of R2ucare through its application to real data.

scholarly journals STATISTICAL INFERENCE FOR COPULAS IN HIGH DIMENSIONS: A SIMULATION STUDY

2013 ◽  
Vol 43 (2) ◽  
pp. 81-95 ◽  
Author(s):  
Paul Embrechts ◽  
Marius Hofert
Keyword(s):  
Statistical Inference ◽  
Large Scale ◽  
Goodness Of Fit ◽  
Point Of View ◽  
Simulation Studies ◽  
High Dimensions ◽  
Research Papers ◽  
Goodness Of Fit Tests ◽  
Interval Estimators ◽  
Theoretical Results

AbstractStatistical inference for copulas has been addressed in various research papers. Due to the complicated theoretical results, studies have been carried out mainly in the bivariate case, be it properties of estimators or goodness-of-fit tests. However, from a practical point of view, higher dimensions are of interest. This work presents the results of large-scale simulation studies with particular focus on the question to what extent dimensionality influences point and interval estimators.

scholarly journals Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 47
Author(s):  
Zakeel ◽  
Geering ◽  
Akinsanmi
Keyword(s):  
Binary Data ◽  
Large Scale ◽  
Goodness Of Fit ◽  
Temporal Dynamics ◽  
Statistical Significance ◽  
Distribution Patterns ◽  
R Package ◽  
Point Pattern ◽  
Temporal And Spatial Distribution ◽  
Vertical Growth

Abnormal vertical growth (AVG) syndrome, which has an unknown aetiology, is a serious threat to the Australian macadamia industry. AVG is characterized by vigorous upright growth and reduced flowering and nut set that results in over 70% yield loss. However, there is a deficiency in knowledge about the distribution of AVG. In this study, we used spatial analysis to provide insights into the distribution and spread of AVG in commercial macadamia orchards in Australia. Using binary data of AVG occurrence from large-scale surveys of six affected commercial orchards in Queensland (five orchards) and New South Wales (one orchard) in 2012 and 2018, spatio-temporal dynamics of AVG was evaluated. Data were subjected to point-pattern and geostatistical analyses using the R package EPIPHY. The Fisher’s index of dispersion of all orchards showed aggregated patterns of affected trees in both years, with statistical significance (p < 0.01) of chi-square test. Goodness-of-fit comparisons of incidence data of all orchards with β-binomial distributions showed that AVG incidence increased by 64% over the six-year period. AVG distribution and the β-binomial parameters exhibited strong heterogeneity, which indicates high degree of aggregation and increasing spread of AVG over time. In addition, binary power law and spatial hierarchy tests confirmed the patterns of aggregation in all orchards. These results implicate a biotic agent as the cause of AVG.

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