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24 results for “multiple regression”
Investigating terrestrial isopod abundance in sandplain grassland using a multiple linear regression
<p>Most North American species of terrestrial isopod (Isopoda) have been introduced from Europe. Sandplain grassland is a globally rare habitat that is abundant on Nantucket Island, Massachusetts and the abundance of terrestrial isopods in the habitat has never been studied. The objective of this project was to develop a model to explain isopod abundance based on vegetation characteristics within Sandplain grassland and use this model to test for land management effects (prescribed burning and mowing) on isopod abundance. I counted terrestrial isopods from 175 pitfall traps set for one week and used multiple linear regression with several selection algorithms to select the best model. The vegetation characteristics I used as regressors do not appear to explain terrestrial abundance well and the final model only contains the percent grass coverage as a regressor. The model suggests that terrestrial isopods decrease in abundance with increasing grass coverage and it explains 29 percent of the data. When management effects are incorporated, the model suggests that mowing significantly increases isopod abundance.</p> <p>Funding for this project came from the Nantucket Islands Land Bank, Nantucket Land Council, and the Nantucket Biodiversity Initiative.</p> <p>Associated vegetation data is in the published "Effects of Sandplain Grassland Management on Spider Richness and Abundance on Nantucket Island" dataset. Sampling methods are in the thesis linked from that dataset.</p> <p>allisopodData.csv - isopod counts by trap<br> dataDictionary.csv - descriptions of variables<br> mckenna-foster_2009.pdf - a report submitted to NBI and used as part of a statistics class at the University of Wisconsin-Green Bay</p> <p> </p>
Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method
<p>This dataset contains a GitHub repository containing all the data, analysis, Nextflow workflows and Jupyter notebooks to replicate the manuscript titled "Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method".</p> <p>It also contains the Multiple Sequence Alignments (MSAs) generated and well as the main figures and tables from the manuscript.</p> <p>The repository is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`.</p> <p>For details on how to use the regressive alignment algorithm, see the T-Coffee software suite (https://github.com/cbcrg/tcoffee).</p>
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression
<p>Dataset accompanying figures published in the publication DOI: https://doi.org/10.5281/zenodo.5930575</p>
Fig. 2 in Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton
Fig. 2. Osteological measurements used in the regression analyses, illustrated on the bones of a postcranial skeleton of Ursus maritimus. A. Femur in anterior (A1) and lateral (A2) views, and in the posterior (A3), medial (A4), and lateral (A5) views of the distal epiphysis. B. Tibia in anterior (B1) and lateral (B2) views. C. Humerus in anterior (C1) and lateral (C2) views. D. Ulna in anterior (D1) and lateral (D2) views. E. Radius in anterior (E1) and lateral (E2) views. For abbreviations and definitions of measurements, see Table 3.
Fig. 4 in Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton
Fig. 4. Box plots of the residuals (log−scale) derived from multiple regression functions. A. Residuals derived from the cranium regression. B. Residuals derived from the mandible regression. C. Residuals derived from the radius regression. D. Residuals derived from the ulna regression. E. Residuals derived from the tibia regression F. Residuals derived from the humerus regression. G. Residuals derived from the femur regression. Vertical lines inside the boxes are the medians. Box length is the interquartile range (IQR) and shows the difference between the 75th and 25th percentiles. Horizontal bars include the largest and smallest values (5–95% confidence limits). Black dots are outliers. Dark grey tones represent the family Ursidae and light grey tones the family Canidae.
Fig. 6 in Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton
Fig. 6. Mean values of body mass (y−axis, log10−scale) estimated for all amphicyonids included in this study. Each amphicyonid species is represented by a symbol positioned at the midpoint of its stratigraphic range (x−axis; data from Hunt 1998, 2001, 2002, 2003, 2009; Peigné et al. 2006). Timescale (in Ma) from Prothero (1998).
Figueirido, B., Pérez−Claros, J.A., Hunt, R.M. Jr., and Palmqvist, P. 2011. Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton. Acta Palaeontologica Polonica 56 (2): 225–246. in Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton
Figueirido, B., Pérez−Claros, J.A., Hunt, R.M. Jr., and Palmqvist, P. 2011. Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton. Acta Palaeontologica Polonica 56 (2): 225–246.
Fig. 5 in Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton
Fig. 5. Reconstruction of three extinct beardogs (right column) compared with their presumed analogues or ecomorphs among the living caniforms (left column). A. Ursus arctos. B. Canis lupus. C. Canis latrans. D. Ysengrinia americana. E. Daphoenodon superbus. F. Daphoenus vetus. Note the three different size classes among these caniforms, and the three types of ecomorphs mentioned in the text. Drawings by Óscar San−Isidro.
Fig. 3 in Body mass estimation in amphicyonid carnivoran mammals: A multiple regression approach from the skull and skeleton
Fig. 3. Bivariate plots with the scores of 442 specimens on the bivariate craniodental morphospaces depicted by the first three principal components. A. Morphospace depicted from first (x−axis) and second (y−axis) principal components. B. Morphospace depicted from second (x−axis) and third (y−axis) principal components.
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
GCTB SBayesR shrunk sparse linkage disequilibrium matrices for HM3 variants, summary statistics and predictors generated from "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019.
<p>GCTB LD matrices and results for HapMap 3 variants and 2.8M variants, which were used for</p> <p>simulation, cross-validation and across biobank analyses in the manuscript "Improved polygenic</p> <p>prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al.</p> <p>2019.</p> <p>Unzip and see README for further details.</p>
Integrating multiple field measurements in a Bayesian parallel regression framework to estimate Tasmanian devil age
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Data from: Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation
Understanding the effects of landscape heterogeneity on spatial genetic variation is a primary goal of landscape genetics. Ecological and geographic variables can contribute to genetic structure through geographic isolation, in which geographic barriers and distances restrict gene flow, and ecological isolation, in which gene flow among populations inhabiting different environments is limited by selection against dispersers moving between them. Although methods have been developed to study geographic isolation in detail, ecological isolation has received much less attention, partly because disentangling the effects of these mechanisms is inherently difficult. Here, I describe a novel approach for quantifying the effects of geographic and ecological isolation using multiple matrix regression with randomization. I explored the parameter space over which this method is effective using a series of individual-based simulations and found that it accurately describes the effects of geographic and ecological isolation over a wide range of conditions. I also applied this method to a set of real-world datasets to show that ecological isolation is an often overlooked but important contributor to patterns of spatial genetic variation and to demonstrate how this analysis can provide new insights into how landscapes contribute to the evolution of genetic variation in nature.
Figure S1: Nutrient concentration of lettuce (Lactuca sativa 'Rex') plants grown at different total incident light levels in deep water culture hydroponics. Lines show multiple regression analysis results, indicating no significant interactions. Each data point represents one plant. N = nitrogen, P = phosphorus, K = potassium, Ca = calcium, Mg = magnesium, S = sulfur, B = boron, Cu = copper, Fe = iron, Mn = manganese, and Zn = zinc.
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Synthetic data set to evaluate and benchmark the performance of multiple linear regression algorithms in Scikit-Learn and SANElib
<p>The datasets respresent different numbers of columns and rows to measure the scalability of linear regression algorihms in terms of columns and rows.</p>
Output - Results of Random Forest and Multiple Linear regression analysis.
<p><strong>Hybrid streamflow modelling using machine learning and multi-model combination.</strong></p> <p> </p> <p><strong>Structure:</strong></p> <p><strong>MLR_output:</strong></p> <ul> <li>Validate <ul> <li>Different setups</li> </ul> </li> </ul> <p><strong>RF_output:</strong></p> <ul> <li>tune <ul> <li><em>all_stations</em></li> </ul> </li> <li>train <ul> <li><em>Different setups</em></li> </ul> </li> <li>Validate <ul> <li><em>Different setups</em></li> </ul> </li> </ul>
Data from: Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation
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Data from: Using multiple imputation to estimate missing data in meta-regression
1. There is a growing need for scientific synthesis in ecology and evolution. In many cases, meta-analytic techniques can be used to complement such synthesis. However, missing data is a serious problem for any synthetic efforts and can compromise the integrity of meta-analyses in these and other disciplines. Currently, the prevalence of missing data in meta-analytic datasets in ecology and the efficacy of different remedies for this problem have not been adequately quantified. 2. We generated meta-analytic datasets based on literature reviews of experimental and observational data and found that missing data were prevalent in meta-analytic ecological datasets. We then tested the performance of complete case removal (a widely used method when data are missing) and multiple imputation (an alternative method for data recovery) and assessed model bias, precision, and multi-model rankings under a variety of simulated conditions using published meta-regression datasets. 3. We found that complete case removal led to biased and imprecise coefficient estimates and yielded poorly specified models. In contrast, multiple imputation provided unbiased parameter estimates with only a small loss in precision. The performance of multiple imputation, however, was dependent on the type of data missing. It performed best when missing values were weighting variables, but performance was mixed when missing values were predictor variables. Multiple imputation performed poorly when imputing raw data which was then used to calculate effect size and the weighting variable. 4. We conclude that complete case removal should not be used in meta-regression, and that multiple imputation has the potential to be an indispensable tool for meta-regression in ecology and evolution. However, we recommend that users assess the performance of multiple imputation by simulating missing data on a subset of their data before implementing it to recover actual missing data.
FIGURE 2 in Regression of dark color in subterranean fishes involves multiple mechanisms: response to hormones and neurotransmitters
FIGURE 2 | Dose-response curves for MCH in melanophores of the species of Pimelodella. Each point is the mean ± SEM (P. transitoria, n = 6, P. speleae, n = 7, and P. kronei, n = 8). Control – melanosomes fully dispersed.
Data from: Multiple regression modelling for estimating endocranial volume in extinct Mammalia
The profound evolutionary success of mammals has been linked to behavioral and life-history traits, many of which have been tied to brain size. However, studies of the evolution of this key trait have yet to explore the full potential of the fossil record, being limited by the difficulty of obtaining endocranial data from fossils. Using measurements of endocranial volume, length, height, and width of the braincase in 503 adult specimens from 199 extant species, representing 99 of 133 extant mammalian families, we expand upon a simple method of using multiple regression to develop a formula for estimating brain size from external skull measurements. We also examined non- mammalian synapsids to assess the phylogenetic limits of our model's application. Model-predicted volume correlates strongly with measured volume (R2 = 0.993) and prediction error is between 16% and 19%. Error decreases if models developed for well-sampled subclades such as primates or rodents are used, demonstrating that some differential evolution of the relationship between brain size and skull size has occurred. However, reanalysis using phylogenetically independent contrasts demonstrates weak phylogenetic dependency, indicating that our model is appropriate for estimating the endocranial volume of species of unknown phylogenetic affinity. Thus, the model represents a generally applicable, fast and cost-efficient way to dramatically expand the taxonomic and temporal scope of mammalian brain size data sets. Even endocranial volumes of taxa with highly derived crania, such as cetaceans and monotremes, can be estimated confidently. However, the model works best for generalized placental crania. Fundamental differences in cranial architecture suggest that the model cannot provide accurate estimates of endocranial volume in non-mammalian synapsids more basal than Morganucodon (ca. 200 Ma). Therefore, use of the model for taxa phylogenetically distant from the mammalian crown group is not warranted, but it might be used to establish relative brain sizes between closely related subgroups.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.