scholarly journals ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency

2019 ◽  
Author(s):  
Elodie Marie Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
Behavioral Science ◽  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Raw Data ◽  
New Approach ◽  
Multiple Variables ◽  
Data Transparency ◽  
Sample Representation ◽  
Statistical Indicators

Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small sample representation. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of a behavior and/or process observations. In this paper, we present ViSiElse, a R-package that offers a new approach in raw data visualization. Methods. This visualization tool was developed as a package of the open-source software R to provide a solution to both the lack of tools allowing visual insights of a whole dataset and the lack of innovative tools for raw data transparency. Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps and all participants in a single graph. Individuals and/or group behavior can easily be assessed and supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse allows a global visualization of actions, acquired from timestamps in any quantifiable observations.

scholarly journals ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency

2019 ◽  
Author(s):  
Elodie Marie Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
Behavioral Science ◽  
R Package ◽  
Group Behavior ◽  
Small Samples ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Multiple Variables ◽  
Statistical Indicators ◽  
Single Graph

Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small samples. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of the behavior or the process observations. In this paper, we present ViSiElse, an R-package offering a new approach in raw data visualization. Methods. ViSiElse was developed with the open-source software R to provide a solution for the complete visualization of the raw time data. Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse provides a global visualization of actions acquired from timestamps in any quantifiable observations.

scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: an innovative R-package to visualize raw behavioral data over time

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8341
Author(s):  
Elodie M. Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

Child sexual abuse and the pathophysiology of suicide in adolescents and adults

2013 ◽  
Vol 25 (3) ◽  
pp. 201-205 ◽  
Author(s):  
Betsy S. O’Brien ◽  
Leo Sher
Keyword(s):  
Sexual Abuse ◽  
Child Sexual Abuse ◽  
Suicidal Behavior ◽  
Behavioral Science ◽  
Risky Behaviors ◽  
Conduct Disorders ◽  
Post Traumatic Stress ◽  
Adolescents And Adults ◽  
Axis I ◽  
Multiple Variables

Abstract Background: Child sexual abuse (CSA) is widespread and is associated with various psychopathologies, including Axis I and II disorders, maladaptive and impulsive behaviors, and suicidal behavior in adolescence and adults. The pathophysiology of this association is not well understood; however, it is clear that suicidal behavior in individuals with a history of CSA is a significant social and medical problem that warrants further investigation. Methods: An electronic search of the major behavioral science databases (limited to the most recent studies in the last 20 years) was conducted to retrieve studies detailing the social, epidemiological, and clinical characteristics of child sexual trauma and their relation to suicidal behavior in adolescents and adults. Results: Studies indicate that CSA is related to an increase in Axis I and II diagnoses, including depression, post-traumatic stress disorder, conduct disorders, eating disorders, alcohol and drug abuse, panic disorders, and borderline personality disorder. CSA not just related to an increase in impulsivity and risky behaviors, it has also been linked to an increase in suicidality as well. Conclusion: CSA makes both direct and indirect contributions to suicidal behavior. It is a complex process involving multiple variables, which include psychopathology, maladaptive personality features and the direct contribution of CSA itself. Psychopathologies, such as impulsivity and mood and personality disorders, may modulate the relationship between CSA and suicidal behavior. Some preventive measures for decreasing the prevalence of CSA and suicidality may include education as well as increased access to mental health services.

IPDfromKM: Reconstruct Individual Patient Data from Published Kaplan-Meier Survival Curves

2020 ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee
Keyword(s):  
Survival Data ◽  
Individual Patient Data ◽  
Secondary Analysis ◽  
R Package ◽  
Patient Data ◽  
Survival Curves ◽  
Raw Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Shiny Application

Abstract BackgroundWhen applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to the IPD. We aim to propose a straightforward and robust approach to help researchers to obtain IPD from published survival curves with a friendly software platform. ResultsImproving upon the existing methods, we proposed an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we develop the R package IPDfromKM and an accompanied web-based Shiny application. Both the R package and Shiny application can be used to extract raw data coordinates from published K-M curves, reconstruct IPD from data coordinates extracted, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, as well as hazard ratios. ConclusionsIPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD data and advance the use of secondary data to make informed decision in medical research.

A New Approach to Estimating the Relation between Audit Fees and Financial Misconduct

2019 ◽  
Vol 34 (2) ◽  
pp. 41-61 ◽  
Author(s):  
Bidisha Chakrabarty ◽  
Scott Duellman ◽  
Michael A. Hyman
Keyword(s):  
Selection Bias ◽  
Audit Fees ◽  
Governance Structure ◽  
Small Sample ◽  
New Approach ◽  
Audit Firm ◽  
Public Firms ◽  
Financial Misconduct ◽  
Systematic Selection ◽  
The Impact

SYNOPSIS Research on the association between abnormal audit fees (measuring audit effort) and financial misconduct has produced mixed results. The use of actual misstatements in this research creates small-sample inferences, introduces systematic selection bias, and reduces the scope of sample coverage. In this study we use a metric based on Benford's Law to analyze the impact of abnormal audit fees on the likelihood of misconduct. This measure is parsimonious, avoids selection bias, and can be computed for a large sample of public firms. Consistent with theory, we find that greater audit effort reduces the likelihood of misconduct and auditor resignations are more likely for clients with higher misconduct likelihood. Our findings are not driven by audit firm size, client size, the governance structure of the client, or economic bonding explanations. The effect is not subsumed when controlling for alternative misconduct measurement metrics and is robust across multiple tests to address endogeneity. JEL Classifications: G32; M41.

Bayesian jackknife empirical likelihood

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 981-988
Author(s):  
Y Cheng ◽  
Y Zhao
Keyword(s):  
Bayesian Inference ◽  
Empirical Likelihood ◽  
Small Sample ◽  
New Approach ◽  
U Statistics ◽  
Posterior Inference ◽  
Jackknife Empirical Likelihood ◽  
General Tool ◽  
Control Study ◽  
Better Than

Summary Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. Lazar (2003) showed that in Bayesian inference, if one replaces the usual likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of $U$-statistics. We propose the so-called Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows that the small-sample performance of the proposed method is better than its frequentist counterpart. Analysis of a case-control study for pancreatic cancer is used to illustrate the new approach.

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 The Reliability of Numerical Modeling in Remote Sensing Data Analysis

2020 ◽  
Vol 149 ◽  
pp. 02012
Author(s):  
Boris Dobronets ◽  
Olga Popova
Keyword(s):  
Empirical Distribution ◽  
Remote Sensing Data ◽  
Small Sample ◽  
Probability Density Functions ◽  
Distribution Functions ◽  
Data Uncertainty ◽  
Computational Statistics ◽  
New Approach ◽  
Interpolation Polynomials ◽  
Empirical Distribution Functions

The article deals with the problem of calculating reliable estimates of empirical distribution functions under conditions of small sample and data uncertainty. To study these issues, we develope computational probabilistic analysis as a new area in computational statistics. We propose a new approach based on random interpolation polynomials and order statistics. Arithmetic operations on probability density functions and procedures for constructing the probabilistic extensions are used.

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