scholarly journals Languages, evolution and statistics: human sound systems were shaped by changes in bite configuration. Response to Tarasov & Uyeda (2020)

2020 ◽  
Author(s):  
Damián E. Blasi ◽  
Steven Moran ◽  
Scott R. Moisik ◽  
Paul Widmer ◽  
Dan Dediu ◽  
...  
Keyword(s):  
Linear Regression ◽  
Model Comparison ◽  
Phylogenetic Analyses ◽  
Statistical Evidence ◽  
Causal Models ◽  
Statistical Analyses ◽  
List Type ◽  
Area Variation

AbstractIn Blasi et al. (2019) we have shown, through a series of statistical analyses and models, that human sound systems have been affected by a transition in bite configuration starting from the Neolithic. Tarasov and Uyeda (2020) (henceforth T&U) raise a number of observations in relation to our article. We appreciate T&U’s engagement with our work and their sharing of the code and data of the analyses reported. In brief, their technical comment involves five analyses:Binomial Causal Models (BCM)Linear Regression of across-area variation in labiodentals and subsistencePredictive Posterior Simulations (PPS)Poisson Linear Regression (PLR): model comparisonPhylogenetic AnalysesIn what follows, we show that the discrepancies they report between our findings and theirs are due mostly to ill-specified models, weak (or missing) statistical evidence, and a misinterpretation of our results. After these issues are addressed, we conclude that T&U’s claims do not hold.

Abstract P051: Inflammation is Significantly Associated With High Blood Pressure in Females Only

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Jenny Eckner ◽  
Charlotte A Larsson ◽  
Lennart Rastam ◽  
Ulf Lindblad
Keyword(s):  
Insulin Resistance ◽  
Blood Pressure ◽  
Body Weight ◽  
Linear Regression ◽  
High Blood Pressure ◽  
Statistical Analyses ◽  
Interaction Terms ◽  
Relative Body Weight ◽  
Males And Females ◽  
Hs Crp

INTRODUCTION The causes of high blood pressure are complex and based on an interaction between multiple biological factors and behaviours. Insulin resistance and inflammation are commonly acknowledged mechanisms in the development of CVD, while insulin resistance and relative body weight correspondingly predict the development of high blood pressure. HYPOTHESIS We aimed to compare insulin resistance, relative body weight, and inflammation in the association with SBP. METHODS In 2001-2005 a random sample of residents aged 30-74 years in the municipalities of Vara and Skövde, South-western Sweden, were invited to a survey of cardiovascular risk factors. In all 1811 participants in Vara (participation rate 81%) and 1005 participants in Skövde (70%) were enrolled. Subjects with a known history of hypertension were excluded for the current study. Specially trained nurses saw all subjects in the morning after a 10 hours over night fast, and venous blood samples were taken. A physical examination included body height and body weight (light cloths and no shoes), blood pressure was measured twice in a supine position after a 5 minutes rest (arm in heart level). The mean of the 2 measurements was used for statistical analyses. Hs-CRP and plasma insulin were analysed, and BMI and HOMA-index were calculated using standard algorithms. The log form of HOMA-ir was used in statistical analyses. Associations were explored in males and females separately using multivariate linear regression. RESULTS In all 2538 subjects, 1266 men (50%) and 1272 women (50%) without known hypertension were included. BMI and HOMA-ir were both significantly associated with SBP in both males and females, while hs-CRP was associated with SBP in women only. These factors were accordingly entered into a multivariate linear regression model also including age. In men HOMA-ir [regression coefficient, (95% confidence interval), and p-value] [5.4 (2.5-8.4), p<0.001], was significantly associated with SBP, while BMI [0.2 (-0.3-0.5), p=0.087], and CRP was not [0.02 (-0.1-0.1), p=0.138]. In women all three mechanisms came out significantly; HOMA-ir [5.4 (2.2-8.6), p<0.001], BMI [0.4 (0.2-0.5), p<0.001], and CRP [0.2 (0.02-0.4), p=0.031]. There were statistically significant interaction terms between gender and CRP (p=0.037), and gender and HOMA-ir (P=0.045), respectively, while no corresponding interaction was found for BMI. CONCLUSIONS Our study confirms a strong impact of insulin resistance and relative body weight on blood pressure levels in both men and women. However, a significant association between hs-CRP and systolic blood pressure in women was not seen in men. Gender differences in insulin resistance and inflammation were statistically confirmed by interaction terms. These findings have implications for future research and for development of clinical practice.

Metabolism and evaporative water loss of Western Australian geckos (Reptilia : Sauria : Gekkonomorpha)

2000 ◽  
Vol 48 (2) ◽  
pp. 111 ◽  
Author(s):  
P. C. Withers ◽  
K. P. Aplin ◽  
Y. L. Werner
Keyword(s):  
Linear Regression ◽  
Body Mass ◽  
Water Use ◽  
Water Loss ◽  
Phylogenetic Analyses ◽  
Resting Metabolic Rate ◽  
Evaporative Water Loss ◽  
Evaporative Water ◽  
Regression Residuals ◽  
Western Australian

Resting metabolic rate (RMR) and evaporative water loss (EWL) were measured, and resistance (R) to evaporative water loss and water use index (WUI = EWL/RMR) were calculated, for 22 species of Western Australian gecko. For all available gecko data, body mass and temperature explained 85% of the variability in RMR (=14.5 mass0.833 100.0398 Ta µL h–1), and 70% of the variability in EWL (=0.126 mass0.539 100.049 Ta mg h–1 ). For Western Australian geckos, RMR and EWL were significantly influenced by body mass, using conventional regression and phylogenetic analyses. Resistance to evaporative water loss (R) was not significantly affected by body mass. Water use index was inversely related to body mass: WUI = 21.9 M–0.344 mg mL O2–1. There were significant differences between species for R and for standardised residuals of RMR, EWL and WUI. R was not correlated with phylogeny, and was significantly higher (P = 0.020) for saxicolous geckos (1467 s cm-1) than terrestrial geckos (797 s cm–1); arboreal geckos had an intermediate R (977 s cm–1). Species that ate termites had lower standardised linear regression residuals (P = 0.003) for RMR than did species that ate more general diets. Standardised residuals for EWL were almost significantly related to microhabitat (P = 0.053). Standardised residuals for WUI were significantly related to microhabitat (P = 0.016); saxicolous species had lower WUI than terrestrial species. Standardised linear regression residuals of the residuals from autoregression (which should be independent of both mass and phylogeny effects) still significantly correlated RMR and diet, but not EWL or WUI with microhabitat.

scholarly journals Linear Markovian models for lag exposure assessment

2018 ◽  
Vol 55 (2) ◽  
pp. 179-195 ◽  
Author(s):  
Alessandro Magrini
Keyword(s):  
Linear Regression ◽  
Exposure Assessment ◽  
Causal Models ◽  
Causal Effects ◽  
Regression Coefficients ◽  
Standard Approach ◽  
General Context ◽  
Time Lags ◽  
Markovian Models ◽  
Distributed Lag

SummaryLinear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem.

scholarly journals Challenges and Solutions for Data Analysis in an Adult Lifespan Study of over 100,000 Online Cognitive Test Completions

2021 ◽  
Author(s):  
Annalise Aleta LaPlume
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Data Visualization ◽  
Model Comparison ◽  
Data Cleaning ◽  
Cognitive Task ◽  
Cognitive Test ◽  
Size Estimation ◽  
Adult Lifespan ◽  
Visualization Techniques

Note: This pre-print includes the accepted version of a manuscript that will be published in Sage Research Methods: Doing Research Online. In this case study, I describe methodological insights from data analysis of an online adult lifespan dataset (over 100,000 completions, ages 15-100). The data were used to study cross-sectional age differences in cognitive performance. I cover the steps of data analysis for large-scale web-based data, namely data cleaning, analysis, and visualization techniques. In each step, I describe the unique challenges that face analysis of data collected online, and potential solutions to address them, by drawing on practical lessons and examples from this study. First, I address how to identify problematic recordings such as technical issues (incomplete data, multiple completions by the same person, etc.), unreliable self-reported demographic information (age), and cognitive task outliers (accuracy, response times). I propose rigorous data cleaning as an essential first step to ensure that analytical conclusions are reliable and unbiased. Next, I demonstrate data visualization techniques that are better suited to large online datasets than more conventional techniques (e.g., density plots or locally weighted scatterplot smoothing instead of dot-plots or linear regression). Lastly, I cover the limitations of significance testing in large online datasets, and the value of complementary approaches such as data visualization, effect size estimation, and use of parsimony criteria. I also discuss more sophisticated analysis options enabled by large online datasets, such as non-linear regression, model comparison and selection, data resampling, and addition of covariates.

Using LiDAR to Measure Alfalfa Canopy Height

2021 ◽  
Vol 64 (6) ◽  
pp. 1755-1761
Author(s):  
S. Tucker Sheffield ◽  
Joe Dvorak ◽  
Bo Smith ◽  
Cynthia Arnold ◽  
Cameron Minch
Keyword(s):  
Linear Regression ◽  
Plant Height ◽  
Predictive Models ◽  
Growth Cycle ◽  
Canopy Height ◽  
List Type ◽  
Field Observations ◽  
Linear Regression Models ◽  
Single Beam ◽  
Lidar Measurements

HighlightsModels using LiDAR measurements and field observations as predictors can accurately predict alfalfa canopy height.The most efficient model used only the 95th percentile of LiDAR-measured height to estimate canopy height.Adding field observations of weed, insect, and disease pressure only marginally improved the predictive models.Abstract. Alfalfa is a popular crop that is grown worldwide because it is a nutritious feed for livestock and fixes nitrogen in the soil. Profitable alfalfa production greatly relies on monitoring the status of the alfalfa crop. Traditionally, producers have used crop assessment techniques that rely on manual measurements of alfalfa plant height, which can be used to predict nutritive quality and yield. These manual measurements are often labor-intensive and provide low-resolution data that is not acceptable for field-scale monitoring. The goal of this study was to assess the capability of a simple LiDAR setup to accurately estimate the average canopy height of an alfalfa crop. To achieve this goal, we first developed predictive models of alfalfa canopy height using LiDAR-derived measurements as predictor variables. Second, we assessed the accuracies of the models and compared the properties of each model. Third, we determined the optimal LiDAR-derived measurements to use to accurately predict average alfalfa canopy height. The data used in our models were collected in two separate fields planted with two different cultivars of alfalfa. Data collection was performed on five dates spanning one entire growth cycle during the summer of 2019. A simple single-beam LiDAR sensor was used to scan the canopy of sample plots within the fields. Manual measurements of plant height and field observations of insect, disease, and weed pressure were also recorded. Of the data used in the predictive models, the 95th percentile of LiDAR-measured height was found to be the optimal predictor for estimating alfalfa canopy height. Using the 95th percentile as a single predictor in a linear regression model of measured average canopy height resulted in an R2 of 0.90 and RMSE of 4.5 cm. Two other linear regression models using a combination of LiDAR measurements and LiDAR measurements with alfalfa health observations, respectfully, were developed for comparison. These models exhibited marginally better accuracies but required more inputs than the model only using the 95th percentile. This study shows how simple LiDAR configurations can be used for timely collection of accurate alfalfa canopy height data. From our findings, we suggest using the 95th percentile of LiDAR-derived canopy height to estimate alfalfa canopy height. This study lays the groundwork for research into more advanced LiDAR configurations for alfalfa applications, such as LiDAR-equipped UAVs. Keywords: Alfalfa, Canopy height, LiDAR.

scholarly journals bModelTest: Bayesian phylogenetic site model averaging and model comparison

2015 ◽  
Author(s):  
Remco R Bouckaert ◽  
Alexei J Drummond
Keyword(s):  
Model Comparison ◽  
Ad Hoc ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Model Averaging ◽  
Sources Of Information ◽  
Nucleotide Sequence Data ◽  
Site Model ◽  
Wide Range ◽  
Substitution Models

AbstractBackground:Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic analyses are popular for interpreting nucleotide sequence data, however for such studies one needs to specify a site model and associated substitution model. Often, the parameters of the site model is of no interest and an ad-hoc or additional likelihood based analysis is used to select a single site model.Results:bModelTest allows for a Bayesian approach to inferring and marginalizing site models in a phylogenetic analysis. It is based on trans-dimensional Markov chain Monte Carlo (MCMC) proposals that allow switching between substitution models as well as estimating the posterior probability for gamma-distributed rate heterogeneity a proportion of invariable sites and unequal base frequencies. The model can be used with the full set of time-reversible models on nucleotides, but we also introduce and demonstrate the use of two subsets of time-reversible substitution models.Conclusion:With the new method the site model can be inferred (and marginalized) during the MCMC analysis and does not need to be pre-determined, as is now often the case in practice, by likelihood-based methods. The method is implemented in the bModelTest package of the popular BEAST 2 software, which is open source, licensed under the GNU Lesser General Public License and allows joint site model and tree inference under a wide range of models.

scholarly journals The Month-of-the-Year Effect in the European, American, Australian and Asian Markets

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 168
Author(s):  
Gualter Couto ◽  
Pedro Pimentel ◽  
Catarina Barbosa ◽  
Rui Alexandre Castanho
Keyword(s):  
Linear Regression ◽  
Statistical Significance ◽  
Statistical Evidence ◽  
Excess Return ◽  
January Effect ◽  
European American ◽  
Average Return ◽  
Year Effect ◽  
Halloween Effect

This paper examines the existence of the month-of-the-year effects in four different continents, namely Europe, Asia, America, and Oceania. Nine indexes were analyzed in order to verify differences between monthly returns from January 1990 to December 2013, followed by an examination of the January effect, Halloween effect, and the October effect, testing for statistical significance using an OLS linear regression in order to verify whether those effects offer consistent opportunities for investors. Investors with globally diversified portfolios benefit from the Halloween effect, with a 1.2% average monthly excess return in winter and spring, while the pre-dotcom-bubble period had a better performance than the post-dotcom-bubble period. In the global post-dotcom-bubble period, there is statistical evidence for 1.60% and 1% lower average monthly returns in January (the January effect) and in months other than October (the October effect), respectively, contradicting the literature. The dotcom bubble seems to be responsible for the January effect differing from what might otherwise have been expected in the later period. There is no consistent and clear impact on continental incidence. The Halloween effect is revealed to be a fruitful strategy in the FTSE, DAX, Dow Jones, BOVESPA, and N225 indexes taken one-by-one. The January effect excess average return was only statistically significative for the pre-dotcom-bubble period for globally diversified portfolios. This paper contributes to a wider global and comparable view upon month-of-the-year effect.

scholarly journals HybPhaser: a workflow for the detection and phasing of hybrids in target capture datasets

2020 ◽  
Author(s):  
Lars Nauheimer ◽  
Nicholas Weigner ◽  
Elizabeth Joyce ◽  
Darren Crayn ◽  
Charles Clarke ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Phylogenetic Signal ◽  
Phylogenetic Analyses ◽  
List Type ◽  
The Novel ◽  
Paralogous Genes ◽  
Target Capture ◽  
Mapping Sequence ◽  
Phylogenetic Framework ◽  
Multiple References

AbstractPremise of the study: Hybrids contain divergent alleles that can confound phylogenetic analyses but can provide insights into parental lineages when identified and phased. We developed HybPhaser to detect hybrids in target capture datasets and to phase reads according to haplotypes based on similarity and a phylogenetic framework.Methods and Results: HybPhaser is an extension to the HybPiper sequence assembly workflow. We used Angiosperms353 target capture data for Nepenthes including known hybrids to test the novel workflow. Reference mapping was used to record heterozygous sites and identify hybrid accessions that were phased by mapping sequence reads to multiple references. The parental lineages of known hybrids were confirmed and conflicting phylogenetic signal reduced, improving the outcomes of phylogenetic analysis.Conclusions: HybPhaser is a novel pipeline for summarizing and optimizing target capture datasets, detecting hybrid accessions as well as paralogous genes, and generating phased accessions that can provide insights into reticulated evolution.

Sign in / Sign up

Export Citation Format

Share Document