scholarly journals phylopath: Easy phylogenetic path analysis in R

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4718 ◽  
Author(s):  
Wouter van der Bijl
Keyword(s):  
Experimental Data ◽  
Path Analysis ◽  
Observational Data ◽  
Brain Size ◽  
R Package ◽  
Causal Models ◽  
Binary Variables ◽  
The Relationship

Confirmatory path analysis allows researchers to evaluate and compare causal models using observational data. This tool has great value for comparative biologists since they are often unable to gather experimental data on macro-evolutionary hypotheses, but is cumbersome and error-prone to perform. I introducephylopath, an R package that implements phylogenetic path analysis (PPA) as described by von Hardenberg & Gonzalez-Voyer (2013). In addition to the published method, I provide support for the inclusion of binary variables. I illustrate PPA andphylopathby recreating part of a study on the relationship between brain size and vulnerability to extinction. The package aims to make the analysis straight-forward, providing convenience functions, and several plotting methods, which I hope will encourage the spread of the method.

scholarly journals phylopath: Easy phylogenetic path analysis in R

2017 ◽  
Author(s):  
Wouter van der Bijl
Keyword(s):  
Experimental Data ◽  
Path Analysis ◽  
Observational Data ◽  
Brain Size ◽  
Causal Models ◽  
List Type ◽  
Binary Variables ◽  
Link Type ◽  
The Relationship

AbstractConfirmatory path analysis allows researchers to evaluate and compare causal models using observational data. This tool has great value for comparative biologists since they are often unable to gather experimental data on macro-evolutionary hypotheses, but is cumbersome and error-prone to perform.I introducephylopath, anRpackage that implements phylogenetic path analysis (PPA) as described by Von Hardenberg & Gonzalez-Voyer (2013). In addition to the published method, I provide support for the inclusion of binary variables.I illustrate PPA andphylopathby recreating part of a study on the relationship between brain size and vulnerability to extinction.The package aims to make the analysis straight-forward, providing convenience functions and several plotting methods, which I hope will encourage the spread of the method.phylopathis released under the GPL-3 license, and is freely available on CRAN (https://cran.r-project.org/web/packages/phylopath/index.html) and GitHub (https://github.com/Ax3man/phylopath).

scholarly journals Consistency and overfitting of multi-omics methods on experimental data

2019 ◽  
Vol 21 (4) ◽  
pp. 1277-1284 ◽  
Author(s):  
Sean D McCabe ◽  
Dan-Yu Lin ◽  
Michael I Love
Keyword(s):  
Experimental Data ◽  
Factor Analysis ◽  
Canonical Correlation ◽  
Cross Validation ◽  
Small Sample Size ◽  
R Package ◽  
Small Sample ◽  
Ready Availability ◽  
Variation Explained ◽  
The Relationship

Abstract Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

scholarly journals Understanding primate brain evolution

2007 ◽  
Vol 362 (1480) ◽  
pp. 649-658 ◽  
Author(s):  
R.I.M Dunbar ◽  
Susanne Shultz
Keyword(s):  
Life History ◽  
Path Analysis ◽  
Group Size ◽  
Brain Size ◽  
Brain Evolution ◽  
Primate Brain ◽  
The Social ◽  
Large Brain ◽  
Brain Data ◽  
The Relationship

We present a detailed reanalysis of the comparative brain data for primates, and develop a model using path analysis that seeks to present the coevolution of primate brain (neocortex) and sociality within a broader ecological and life-history framework. We show that body size, basal metabolic rate and life history act as constraints on brain evolution and through this influence the coevolution of neocortex size and group size. However, they do not determine either of these variables, which appear to be locked in a tight coevolutionary system. We show that, within primates, this relationship is specific to the neocortex. Nonetheless, there are important constraints on brain evolution; we use path analysis to show that, in order to evolve a large neocortex, a species must first evolve a large brain to support that neocortex and this in turn requires adjustments in diet (to provide the energy needed) and life history (to allow sufficient time both for brain growth and for ‘software’ programming). We review a wider literature demonstrating a tight coevolutionary relationship between brain size and sociality in a range of mammalian taxa, but emphasize that the social brain hypothesis is not about the relationship between brain/neocortex size and group size per se ; rather, it is about social complexity and we adduce evidence to support this. Finally, we consider the wider issue of how mammalian (and primate) brains evolve in order to localize the social effects.

Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

Simulation on Extrusion Comminution Model of Roller Press

2010 ◽  
Vol 156-157 ◽  
pp. 1702-1707
Author(s):  
Xiang Wen Cheng ◽  
Jinchao Liu ◽  
Qi Zhi Ding ◽  
Li Ming Song ◽  
Zhan Lin Wang
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Field Experiments ◽  
Size Ratio ◽  
Optimum Operating ◽  
Extrusion Force ◽  
Particle Size Ratio ◽  
Small Constant ◽  
Working Parameters ◽  
The Relationship

How to predict the relationship among particle size and among product size, to establish the relationship between the granularity and working parameters in the process of grinding and to determine the optimum operating parameters. With proposing BS squeeze crush model by L. Bass and the idea of roll surface division as the material uneven extrusion force are adopted. Based on field experiments the experimental data is analyzed, the select function and the breakage functions are fitted with MATLAB software, and obtaining their model. The comminution model is determined by the roller division. We obtain the model parameter through the experimental data. Through model analysis shows: the relationship between particle breakage and energy absorption, namely the smaller size of the same power, the lower broken; the breakage diminishes with the decrease of particle size ratio and it will be tending to a small constant when the smaller particle size ratio. The breakage functions rapidly decrease within ratio of between 0.2-0.7. This shows: the energy consumption will rapidly increase when the particle size of less than 0.2 in broken; the selection diminish with the decrease of particle size. Pressure (8-9MPa) should be the most appropriate value.

scholarly journals Testing Graphical Causal Models Using the R Package “dagitty”

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ankur Ankan ◽  
Inge M. N. Wortel ◽  
Johannes Textor
Keyword(s):  
R Package ◽  
Causal Models

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Influential Path of Social Risk Factors toward Suicidal Behavior—Evidence from Chinese Sina Weibo Users 2013–2018

2021 ◽  
Vol 18 (5) ◽  
pp. 2604
Author(s):  
Yujin Han ◽  
He Li ◽  
Yunyu Xiao ◽  
Ang Li ◽  
Tingshao Zhu
Keyword(s):  
Risk Factors ◽  
Path Analysis ◽  
Suicide Prevention ◽  
Suicidal Behavior ◽  
Empirical Support ◽  
National Bureau ◽  
Social Risk ◽  
Analysis Model ◽  
The Relationship ◽  
Path Analysis Model

(1) Purpose: The purpose of this study was to determine suicidal risk factors, the relationship and the underlying mechanism between social variables and suicidal behavior. We hope to provide empirical support for the future suicide prevention of social media users at the social level. (2) Methods: The path analysis model with psychache as the mediate variable was constructed to analyze the relationship between suicidal behavior and selected social macro variables. The data for our research was taken from the Chinese Suicide Dictionary, Moral Foundation Dictionary, Cultural Value Dictionary and National Bureau of Statistics. (3) Results: The path analysis model was an adequate representation of the data. With the mediator psychache, higher authority vice, individualism, and disposable income of residents significantly predicted less suicidal behavior. Purity vice, collectivism, and proportion of the primary industry had positive significant effect on suicidal behavior via the mediator psychache. The coefficients of harm vice, fairness vice, ingroup vice, public transport and car for every 10,000 people, urban population density, gross domestic product (GDP), urban registered unemployment rate, and crude divorce rate were not significant. Furthermore, we applied the model to three major economic development belts in China. The model’s result meant different economic zones had no influence on the model designed in our study. (4) Conclusions: Our evidence informs population-based suicide prevention policymakers that incorporating some social factors like authority vice, individualism, etc. can help prevent suicidal ideation in China.

scholarly journals COVID-19 uncertainty and sleep: the roles of perceived stress and intolerance of uncertainty during the early stage of the COVID-19 outbreak

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dan Wu ◽  
Tingzhong Yang ◽  
Daniel L. Hall ◽  
Guihua Jiao ◽  
Lixin Huang ◽  
...  
Keyword(s):  
Path Analysis ◽  
Perceived Stress ◽  
Mediation Analysis ◽  
Early Stage ◽  
Intolerance Of Uncertainty ◽  
Policy Implications ◽  
Self Report ◽  
Poor Sleep ◽  
The Relationship

Abstract Background The COVID-19 pandemic brings unprecedented uncertainty and stress. This study aimed to characterize general sleep status among Chinese residents during the early stage of the outbreak and to explore the network relationship among COVID-19 uncertainty, intolerance of uncertainty, perceived stress, and sleep status. Methods A cross-sectional correlational survey was conducted online. A total of 2534 Chinese residents were surveyed from 30 provinces, municipalities, autonomous regions of China and regions abroad during the period from February 7 to 14, 2020, the third week of lockdown. Final valid data from 2215 participants were analyzed. Self-report measures assessed uncertainty about COVID-19, intolerance of uncertainty, perceived stress, and general sleep status. Serial mediation analysis using the bootstrapping method and path analysis were applied to test the mediation role of intolerance of uncertainty and perceived stress in the relationship between uncertainty about COVID-19 and sleep status. Results The total score of sleep status was 4.82 (SD = 2.72). Age, place of residence, ethnicity, marital status, infection, and quarantine status were all significantly associated with general sleep status. Approximately half of participants (47.1%) reported going to bed after 12:00 am, 23.0% took 30 min or longer to fall asleep, and 30.3% slept a total of 7 h or less. Higher uncertainty about COVID-19 was significantly positively correlated with higher intolerance of uncertainty (r = 0.506, p < 0.001). The mediation analysis found a mediating role of perceived stress in the relationship between COVID-19 uncertainty and general sleep status (β = 0.015, 95%C.I. = 0.009–0.021). However, IU was not a significant mediator of the relationship between COVID-19 uncertainty and sleep (β = 0.009, 95%C.I. = − 0.002–0.020). Moreover, results from the path analysis further showed uncertainty about COVID-19 had a weak direct effect on poor sleep (β = 0.043, p < 0.05); however, there was a robust indirect effect on poor sleep through intolerance of uncertainty and perceived stress. Conclusions These findings suggest that intolerance of uncertainty and perceived stress are critical factors in the relationship between COVID-19 uncertainty and sleep outcomes. Results are discussed in the context of the COVID-19 pandemic, and practical policy implications are also provided.

The effects of capital on an income breeder: evidence from female Columbian ground squirrels

2005 ◽  
Vol 83 (4) ◽  
pp. 546-552 ◽  
Author(s):  
David R Broussard ◽  
F Stephen Dobson ◽  
J O Murie
Keyword(s):  
Path Analysis ◽  
Observational Data ◽  
Life Histories ◽  
Reproductive Investment ◽  
Ground Squirrels ◽  
Initial Capital ◽  
Spermophilus Columbianus ◽  
Live Trapping ◽  
Investment In Reproduction ◽  
Columbian Ground Squirrels

To maximize fitness, organisms must optimally allocate resources to reproduction, daily metabolic maintenance, and survival. We examined multiple years of live-trapping and observational data from a known-aged population of female Columbian ground squirrels, Spermophilus columbianus (Ord, 1815), to determine the influences of stored resources and daily resource income on the reproductive investments of females. We predicted that because yearling females were not fully grown structurally while producing their first litter, they would rely exclusively on income for reproduction, while reproductive investment in older females (≥2 years of age) would be influenced by both stored resources (capital) and daily income. Results from path analysis indicated that both yearlings and older females were income breeders. However, initial capital indirectly influenced investment in reproduction of yearling and older females. Females with the greatest initial capital maintained high body masses while investing relatively more income in reproduction. By considering influences of both capital and income, important relationships can be revealed between these resources and their influence on life histories.

Sign in / Sign up

Export Citation Format

Share Document