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67 results for “Regression analysis”
Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis.
<p>The response variable is High Heating Values (HHV), while the independent variables are Fixed Carbon (FC), Volatile Matter (VM), and Ash (A). </p>
Figure 2 in Inferring life history from ovipositor morphology in parasitoid wasps using phylogenetic regression and discriminant analysis
Figure 2. Distal part of ovipositor of the four parasitoid species whose life history is unknown, plus a selection of species whose life history is known. The complete ovipositor of the four species is also shown in profile, drawn relative to the width of the head of the species. Species with known life history are as follows (see Tables 1 and 2 for full names and classification). Endoparasitoids: host is exposed – a Aleiodes, b Zele, c Mesoleptus, d Megastylus, e Ophion, f Microgaster; host is leafminer – g Dacnusa, h Sathon; host is under fruit skin – i Pristomerus; host is in decaying fruit – j Asobara; wasp probes for deeply concealed host – k Orgilus, l Rhimphoctona, m Eubazus; host is stem-borer – n Collyria; host is gall-former – o Orthopelma. Ectoparasitoids: wasp probes for deeply concealed host – p Pseudorhyssa, q Stenobracon; host is leaf-miner – r Colastes; wasp bores for deeply concealed host – s Coeloides.
Figure 4 in Inferring life history from ovipositor morphology in parasitoid wasps using phylogenetic regression and discriminant analysis
Figure 4. Plots of biology vs. phylogenetic regression (PR) estimates of biology for the taxa where the biology is known; results from the three best PR models shown. State 0 = taxa are ectoparasitoids and state 1 = taxa are endoparasitoids. Philomacroploea and Mesoleptus, which are wrongly classified with PR, are indicated. The characters used in the models (U, P and H) are explained in Table 3. Estimates are derived as follows: PR (U) = (0.5281 + 0.8821) ¥ (U - 4730). PR (U + P) = 0.5144 + 1.001 ¥ (U - 0.4617) + 0.1531 ¥ (P - 0.1531). PR (U + P + H) = 0.4948 + 0.9818 ¥ (U - 0.4456) + 0.7428 ¥ (P - 0.1571) - 10.17 ¥ (H - 0.0054).
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel's lambda model
<p class="MsoNormal"><span>Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman's rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel's <em>λ</em> model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, <em>R</em><sup>2</sup>, <em>p</em>-value, Pagel's <em>λ</em>, Blomberg et al.'s <em>K</em>, and the estimated <em>λ</em> in <a name="_Hlk136010442"></a>Pagel's <em>λ</em> model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher <em>λ</em></span><span> or <em>K</em> </span><span>value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.</span></p>
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel’s lambda model
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Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach
All data are used in the following manuscript which is in prep Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach Calef et al.
Data in the analysis of Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective
<p>This data is used in the analysis related to the article Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective. The data contain Crude Oil Prices, Exchange Rates, and Dummy variables coded 1 for the era before the Covid-19 outbreak and 0 for the era of Covid-19.</p> <p> </p>
Julio-Yanes/Yanes_Cannabis_MetaAnalysis: Effects of Cannabinoid Administration for Pain: A Meta-Analysis and Meta-Regression
<p>% The following codes are associated with...</p> <p>% Effects of Cannabinoid Administration for Pain: A Meta-Analysis and Meta-Regression... Yanes, J.A., McKinnell Z.E., Reid, M.A., Busler, J.N.,... ... Michel, J.S., Pangelinan, M.M., Sutherland, M.T., Younger, J.W... ... Gonzalez, R., and Robinson, J.L. (in press). Experimental % Clinical Psychopharmacology.</p> <p>% The following codes load data associated with the above mentioned manuscript, run analyses, and prepare tables/figures.</p> <p>% Catgorical data within the dataset were coded as follows... ... Design (0 = crossover, 1 = parallel). ... Treatment (0 = placebo, 1 = THC, 2 = THC/CBD extract [Sativex],... ... 3 = THC/CBD, 4 = Nabilone, 5 = Dronabinol, 6 = CT3, 7 = CBD). ... Treatment Collapsed (0 = placebo, 1 = cannabis [whole-plant,... ... extract], 2 = synthetic cannabinoid). ... Treatment Binary (0 = placebo, 1 = cannabinoid). ... Diagonsis (1 = abdominal pain, 2 = arthritis, 3 = cancer,... ... 4 = chronic pain, 5 = diabetes, 6 = fibromyalgia, 7 = HIV,... ... 8 = headache, 9 = "various", 10 = multiple sclerosis,... ... 11 = neuropathic pain, 12 = post-operative pain).</p>
Figure 3 in Inferring life history from ovipositor morphology in parasitoid wasps using phylogenetic regression and discriminant analysis
Figure 3. Measurements used in the analyses. See Table 3 for further explanation of characters.
Scripts, inputs and outputs of Module 2 Regression analysis regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 2_Regression analysis (inputs, scripts, and formulations of regression analysis per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Predicting Prime Path Coverage Using Regression Analysis
<p>SBES - Research Track - 2020 - Presentation Video.</p>
Data -SUPPLIMENDARY INFORMATIONS –Growth of D614G Virus – Regression Analysis
<p>Data analysis report for Growth of D614G Virus – Regression Analysis.</p>
Data from: Association between metabolic syndrome components and the risk of developing nephrolithiasis: Bayesian meta-analysis and meta-regression with dose-response analysis
<p>Nephrolithiasis has shifted to be a systemic disease. As opposed to an isolated urinary metabolic problem, it became determined that nephrolithiasis turned into considerably related to link with systemic diseases such as hypertension, obesity, dyslipidemia, and insulin resistance. The interplay between these four factors defines MetS (metabolic syndrome). In this review we aim to clarify the associations of metabolic syndrome and its components to kidney stone incident. Online databases of EMBASE, MEDLINE, and Google Scholar were searched up to October 2020 to identify observational studies examining the association between metabolic syndrome components and kidney stone incident. Bayesian Random-Effects Meta-Analysis and Meta-Regression were performed to observe the association. Linear dose-response analyses were conducted to shape the direction of the association. Data analysis was performed using STATA, and R statistics. This dataset contains supplementary material and figures as additional analysis of the study.</p>
Exploring Factors Promoting Dependency Updates with Survival Time Analysis and Logistic Regression
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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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FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter. in Termite coprolites (Blattodea: Isoptera) from the Early Cretaceous of eastern Inner Mongolia, Northeast China
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter.
Single-incision laparoscopic cholecystectomy versus conventional multi-port laparoscopic cholecystectomy: A systematic review, meta-analysis, and meta-regression of randomized controlled trials
<p>Single-incision laparoscopic cholecystectomy versus conventional multi-port laparoscopic cholecystectomy: A systematic review, meta-analysis, and meta-regression of randomized controlled trials</p>
RESULTS OF TPB REGRESSION ANALYSIS IN HONDURAS
<p>RESULTS OF TPB REGRESSION ANALYSIS IN HONDURAS (ROBUST STANDARD ERRORS ARE IN PARENTHESIS) * P-VALUE < 0.10, ** P-VALUE < 0.05, *** P-VALUE < 0.01</p>
RESULTS OF TPB REGRESSION ANALYSIS IN UGANDA
<p>RESULTS OF TPB REGRESSION ANALYSIS IN UGANDA (ROBUST STANDARD ERRORS ARE IN PARENTHESIS) </p> <p>* P-VALUE < 0.10, ** P-VALUE < 0.05, *** P-VALUE < 0.01</p> <p> </p>
Life-cycle assessment of hydrogen systems: A systematic review and meta-regression analysis
<p>The high expectations placed on hydrogen as a clean fuel have led to a growing amount of life-cycle assessment (LCA) studies of hydrogen-related systems. The multiple methodological choices and diverse technical characteristics contribute to a broad set of practices, resulting in significant variability even among similar systems. This study sets the basis for the development of harmonised guidelines for LCA of fuel cells and hydrogen (FCH) systems by analysing current LCA practices. The reviewed literature suggests that previous efforts on harmonisation of LCA methodological choices have led to common practices for certain choices, e.g. functional unit. However, an incomplete definition of some parameters hinders the interpretability of LCA results and hides the potential sources of variability in terms of LCA estimates. In this work, in addition to a systematic literature review of LCA of FCH systems to identify current practices and gaps, sources of variability were investigated for the life-cycle greenhouse gas emissions of hydrogen production systems through a meta-regression analysis (MRA). The MRA results show that the variability of LCA estimates in the literature can be explained by a limited set of qualitative (e.g. implementation of CO<sub>2</sub> capture, among other technological choices) and quantitative (e.g. electricity consumption for hydrogen processing) variables. Although a certain progress towards common methodological choices in LCA of FCH systems was identified, further work is still needed to harmonise practices, as well as to extend the application of the proposed MRA approach to other life-cycle indicators for both identification of main drivers and harmonisation of LCA impact scores.</p>
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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)
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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.