Open-source Statistical Software Research Articles

Using Rate Transient Analysis and Bayesian Algorithms for Reservoir Characterization in Unconventional Gas Wells during Linear Flow

Summary The main objective of this work is to improve robust, repeatable interpretation of reservoir characteristics using rate transient analysis (RTA). This is to generate probabilistic credible intervals for key reservoir and completion variables. This resulting data-driven algorithm was applied to production data from both synthetic and actual case histories. Synthetic production data from a multistage, hydraulically fractured horizontal completion in a reservoir modeled after the Marcellus Shale reservoir were generated using a reservoir model. The synthetic production data were analyzed using a combination of RTA and Bayesian techniques. First, the traditional log-log plot was produced to identify the linear flow production regime. Using the linear flow production data and traditional RTA equations, Bayesian inversion was carried out using two distinct Bayesian methods. The “rjags” and “EasyABC” packages in the open-source statistical software R were used for the traditional and approximate inversion, respectively. Model priors were based on (1) information available about the Marcellus Shale from technical literature and (2) results from a hydraulic fracturing forward model. Posterior distributions and credible intervals were produced for the fracture length, matrix permeability, and skin factor. These credible intervals were then compared with true reservoir and hydraulic fracturing data. The methodology was also repeated for an actual case in the Barnett shale. The most substantial finding was that for nearly all the investigated cases—including complicated scenarios (such as including finite fracture conductivity, fracturing fluid flowback, and heterogeneity in fracture length in the reservoir/hydraulic fracturing forward model)—the combined RTA-Bayesian model provided a 95% credible interval that encompassed the true values of the reservoir/hydraulic fracture parameters. We also found that the choice of the prior distribution did not affect the posterior distribution/credible interval in a significant manner as long as it was moderately concentrated and consistent with engineering science. Also, a comparison of the approximate Bayesian computation (ABC) and the traditional Bayesian algorithms showed that the ABC algorithm reduced computational time by at least an order of magnitude with minimal loss in accuracy. In addition, the production history used, the number of iterations, and the tolerance of fitting in the ABC analysis had a minimal impact on the posterior distribution after an optimal point, which were determined to be at least 1-year production history, 10,000 iterations, and 0.001, respectively. In summary, the RTA-Bayesian production analysis method was implemented using relatively user-friendly computational platforms [R and Excel® (Microsoft Corporation, Redmond, Washington, USA)]. This methodology provided reasonable characterization of all key variables such as matrix permeability, fracture length, and skin when compared to results obtained from analytical methods. This probabilistic characterization has the potential to enable better understanding of well performance ranges expected from shale gas wells. The methodology described here can also be generalized to shale oil systems during linear flow.

Doctor-patient communication and trust in doctors during COVID 19 times-A cross sectional study in Chennai, India.

The COVID 19 pandemic created a global public health crisis. Physical distancing, masks, personal protective equipment worn by the doctors created difficulties in effective doctor-patient communication. This study was conducted to assess the difficulties faced by patients in communicating with their doctors due to the COVID 19 preventive measures, and its impact on the trust on their doctors. A cross sectional study of 359 persons attending a tertiary care center in Chennai, sampled in a non-probabilistic manner selected from the outpatient department, wards, and isolation facilities, was conducted using a questionnaire containing items covering three dimensions namely difficulties faced in accessing the health facility, difficulties in doctor-patient communication and trust in the doctors. The data were collected using Google Forms and analyzed using GNU PSPP open-source statistical software version 1.4.0. More than 60% of the participants complained of difficulty in accessing the health facility. More than 60% had difficulties in communicating with the doctors. There was a high level of trust in doctors among more than 80% of the participants. Comparison of the mean scores revealed that accessibility was a problem across ages, sexes, education and occupation groups. Communication barriers decreased with age and increased with education, but trust increased with age, but reduced with increasing education. Multivariable linear regression analysis revealed that difficulties in communication had a negative impact on trust (β = -0.63, p<0.001) and increasing education had a negative impact on trust (β = -0.42, p = 0.034). The COVID 19 pandemic and the preventive strategies such as lock-down, physical distancing, face mask and personal protective equipment created barriers to effective doctor patient communication and led to some compromise in trust in doctors during this time.

Porphyry Cu-Au prospectivity modelling using semi-supervised learning algorithm in Dehsalm district, eastern Iran

Porphyry Cu-Au prospectivity modelling using semi-supervised learning algorithm in Dehsalm district, eastern Iran

The emergence of residential satisfaction studies in social research: A bibliometric analysis

This research aims to review the total collection of literature on the ‘residential satisfaction’ concept used in social research from 1961 to 2020, and provides a complete overview on how social research concerning ‘residential satisfaction’ has emerged and developed in the last 60 years. We follow the theoretical framework of bibliometric analysis and use the bibliometric datasets retrieved from Web of Science and Scopus's online resources. After compiling and processing the datasets, we used a final dataset containing 877 documents on residential satisfaction. The open-source statistical and visualisation software packages R and VOSviewer were used mainly for processing and analysing datasets. The results visualise information on the top authors, the most influential documents by citation, the most productive countries, and other criteria. The lexical network analysis shows that residential satisfaction has a strong co-occurrence association with the two frequently occurring terms ‘neighbourhood’ and ‘satisfaction’. The majority of research on residential satisfaction has been conducted in the United States, China, and the United Kingdom. The top three most influential journals for residential satisfaction research are ‘Social Indicators Research’, ‘Environment and Development’, and ‘Habitat International’. This bibliometric analysis frames the future research demand for residential satisfaction in social research.

A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities

This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.

Competence assessment of the clinical tutor: a multicentric observational study.

Background and aim of the study:In the international literature there are no validated tools which investigate clinical tutors’ skills. The main objective of the study has been to describe the clinical nurse tutor’s skills, required to properly train nursing students during their educational path.Methods:In this observational study a non-probability sampling has been used. The study was led in two centers: the AOUPR of Parma and the AUSL of Parma, after obtaining the favorable opinion from the Ethics Committee of the Northern Emilia Large Section. The data have been collected by using a structured and self-given survey that investigated three areas. Each item has a 4-point Likert scale, in which 1 indicates “for nothing” and 4 “very much”. The data have been analyzed with the statistical software IBM SPSS v.26 (R) and with the open-source statistical software Jamovi v.1.6.9 (https://www.jamovi.org.). The number of factors in the original model was reduced using several established research steps and then evaluated for data quality and construct validity using principal component analysis and confimatory factor analysis.Results:Among 397 administered questionnaires, only 300, which were considered valid, have been filled. The psychometric properties of the investigation tool turned out to be good in all the areas analyzed with a Cronbach alpha higher than 0.70. The extensive process resulted in a version with 4 factors.Conclusions:Nurses’ answers have allowed to draw the required profile of the clinical tutors in the different organizational contexts. The results can target possible training proposals to create opportunities for the clinical tutors. (www.actabiomedica.it)

Central venous pressure estimation from ultrasound assessment of the jugular venous pulse.

ObjectivesAcquiring central venous pressure (CVP), an important clinical parameter, requires an invasive procedure, which poses risk to patients. The aim of the study was to develop a non-invasive methodology for determining mean-CVP from ultrasound assessment of the jugular venous pulse.MethodsIn thirty-four adult patients (age = 60 ± 12 years; 10 males), CVP was measured using a central venous catheter, with internal jugular vein (IJV) cross-sectional area (CSA) variation along the cardiac beat acquired using ultrasound. The resultant CVP and IJV-CSA signals were synchronized with electrocardiogram (ECG) signals acquired from the patients. Autocorrelation signals were derived from the IJV-CSA signals using algorithms in R (open-source statistical software). The correlation r-values for successive lag intervals were extracted and used to build a linear regression model in which mean-CVP was the response variable and the lagging autocorrelation r-values and mean IJV-CSA, were the predictor variables. The optimum model was identified using the minimum AIC value and validated using 10-fold cross-validation.ResultsWhile the CVP and IJV-CSA signals were poorly correlated (mean r = -0.018, SD = 0.357) due to the IJV-CSA signal lagging behind the CVP signal, their autocorrelation counterparts were highly positively correlated (mean r = 0.725, SD = 0.215). Using the lagging autocorrelation r-values as predictors, mean-CVP was predicted with reasonable accuracy (r2 = 0.612), with a mean-absolute-error of 1.455 cmH2O, which rose to 2.436 cmH2O when cross-validation was performed.ConclusionsMean-CVP can be estimated non-invasively by using the lagged autocorrelation r-values of the IJV-CSA signal. This new methodology may have considerable potential as a clinical monitoring and diagnostic tool.

Effectiveness of onabotulinumtoxinA (BOTOX) in pediatric patients experiencing migraines: a randomized, double-blinded, placebo-controlled crossover study in the pediatric pain population

IntroductionOnabotulinumtoxinA (OBTA) is approved for treating chronic headaches and migraines in adults, but there is limited scientific literature on the outcomes in pediatric patients. The aim of this study was...

Interpreting the results of clinical trials, embracing uncertainty: A Bayesian approach.

Most clinical trials use null hypothesis significance testing with frequentist statistical inference to report P values and confidence intervals for effect estimates. This method leads to a dichotomisation of results as 'significant' or 'non-significant'. A more nuanced interpretation may often be considered and in particular when the majority of the confidence interval for the effect estimate suggests benefit or harm. In contrast to the frequentist dichotomised approach based on a P value, the application of Bayesian statistics allocates credibility to a continuous spectrum of possibilities and for this reason a Bayesian approach to inference is often warranted as it will incorporate uncertainty when updating our current belief with information from a new trial. The use of Bayesian statistics is introduced in this paper for a hypothetical sepsis trial with worked examples in the R language for Statistical Computing environment and the open-source statistical software JASP. It is hoped that this general introduction to Bayesian inference stimulates some interest and confidence among clinicians to consider applying these methods to the interpretation of new evidence for interventions relevant to anaesthesia and intensive care medicine.

Serac: an R package for ShortlivEd RAdionuclide chronology of recent sediment cores

Short-lived radionuclides are measured in surface sediment to provide a geochronology for the past century. Age-depth models are produced from 210Pbex activity-derived sedimentation rates and corroborated by known events, such as 137Cs and 241Am activities that are result of fallout from nuclear weapon tests and the Chernobyl accident. Different methods of age depth modelling using such data require expertise in lake sedimentation processes.Here, we present a package, serac, that allows the user to compute an age-depth model and generate a graph, the age-depth correspondence in a text file, and metadata, using the free open-source statistical software R. serac ensures the reproducibility of age-depth or age-mass depth models and allows testing of several 210Pbex models (CFCS, CIC, CRS, CRS piecewise) and sedimentation hypotheses (changes in sedimentation rates, presence of instantaneous deposits, varved sedimentation, etc.). Using several case studies, including lakes and lagoon in different environments, we demonstrate the use of the program in diverse situations that may be encountered.The rising number of sediment cores in recent palaeo studies and the need to correlate them require reproducible methods. serac is a user-friendly code that enables age model computation for the past century and encourages the standardisation of outputs.

Characterization and differentiation of aluminum powders used in improvised explosive devices - Part 1: Proof of concept of the utility of particle micromorphometry.

Aluminum (Al) powders are commonly used in improvised explosive devices as metallic fuels, a component of explosive mixtures. These powders can be obtained readily from industrial-scale and consumer products, and produced using unsophisticated "kitchen chemistry" techniques. This research demonstrates the potential of automated particle micromorphometry for comparisons between known source and questioned Al powders recovered from IEDs, as well as for insight into the method of Al powder manufacture. Al powder samples were obtained from legitimate manufacturers, and 56 samples were produced "in-house" from Al-containing spray paints and ball-milled Al foils. Transmitted light microscope images of Al powder particles were acquired using an automated stage with automated z-focus; 17 size and shape parameters were measured for all particles. Approximately 37,000-2,500,000 particles/sample were analyzed using an open-source statistical package with customized code. Dimensionality reduction was required for processing the large datasets: eight of the 17 measured variables were selected based on inspection of the correlation matrix. Data from four subsamples from each of the 56 samples produced using "in-house" methods were analyzed using ANOVA to assess the within- and between-sample variation. High within-sample variation was noted; however, ANOVA and post-hoc Tukey's honestly significant difference (HSD) tests demonstrated that the between-sample variation was substantially larger than the within-sample variation. Each sample could be differentiated from all other samples in the test set. Future experiments will focus on ways to reduce the within-sample variation, and additional statistical and microanalytical methods to classify sources and confidently constrain the method of Al powder manufacture.

Step by step and frame by frame – Workflow for efficient motion tracking of high-speed movements in animals

Analysing the motion of animals, especially at high speeds, is often challenging. Motion tracking software needs to deal with a variety of visual contexts, variable lighting conditions, heterogeneous backgrounds and even background movements. Here we present motion tracking via the easy to use and constantly updated Adobe After Effects software - which is often included in software packages most researchers are already using. The provided custom-made Javascript allows for easy exporting of tracking coordinates. Furthermore, some examples for analysing the obtained data in the open source statistical software 'R' will provide reference points, even for an unexperienced user. We present a step-by-step guide of the methodology using high-speed video recordings of locust jumps and additionally validate this method by successful tracking of simulated data under defined subpar filming conditions. This simulated data allows experienced users to compare the tracking software in use with the here presented workflow to weigh the advantages and disadvantages of any motion tracking software on the market.

A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

L’analyse de variance (ANOVA) est la procédure standard utilisée pour l’inférence statistique dans les plans factoriels. En règle générale, les analyses de variance sont exécutées à l’aide de statistiques fréquentistes, où les valeurs p déterminent la significativité statistique en termes de « tout ou rien ». Ces dernières années, l’approche bayésienne de la statistique inférentielle est de plus en plus considérée comme une alternative légitime à la valeur p . Toutefois, l’adoption généralisée des statistiques bayésiennes, et en particulier de l’ANOVA bayésienne, est limitée par le fait que les concepts bayésiens sont rarement enseignés dans les cours de statistiques appliquées. Par conséquent, les utilisateurs peuvent ne pas savoir comment effectuer une ANOVA bayésienne et en interpréter les résultats. Nous fournissons ici un guide pour réaliser et interpréter une ANOVA bayésienne avec JASP, un logiciel statistique open-source ayant une interface utilisateur graphique. Nous expliquons les concepts clés de l’ANOVA bayésienne à l’aide de deux exemples empiriques.

Mass spectrometry analysis of protein blood extracts of animals with experimental brucellos

Aim. The aim of the present research was to study the possibility of direct detection of the causative agent of brucellosis in a biomaterial under experimental conditions via the MALDI-TOF MS method using Mass-Up program resources and a set of packages for open-source statistical software R. Materials and methods. We used laboratory mice infected with the causative agents of Brucellosis (strains B. melitensis 548, B. abortus 544, B. suis 1330) as models. Protein profiling was performed on a MALDI-TOF Microflex «Bruker Daltonics» mass spectrometer. Results. The bioinformatic-statistical approach used for analyzing MALDI-TOF mass spectra allows to carry out a direct detection of Brucella in the biomaterial; besides, it is possible to determinate their species via the identification of a group of biomarkers. Conclusion. It was experimentally confirmed that the protein profiles of the blood extracts of infected animals contain 11 markers, including 6 genus specific for Brucella spp., which can be associated with Brucella infection.

Determination of Model Structure from Flight Test with Generalized Additive Models

The determination of an appropriate structure for an aerodynamic model developed from flight-test data can be challenging, particularly if a physical interpretation of the model is required. This paper introduces generalized additive models (GAMs) as an efficient and effective means to this end. GAMs provide an intuitive method to determine the structure by allowing the various aerodynamic effects to be separated into an additive model. Individual effects can be modeled by a number of nonlinear smooth functions. The suitability of this method is demonstrated on the example of a simple pitching moment coefficient model that is developed using flight-test data. The ability to separate and visualize effects together with a number of statistical indicators, the relatively low computational costs, and the availability of efficient software packages make GAMs attractive for practical applications. For the work presented in this paper, the “mgcv” package of the open-source statistical software R is used.

Experiential Statistics Learning with RStudio: Study on Students’ Engagement

The aim of this paper was to determine students’ statistics engagement level in an experiential statistics learning process with RStudio (an open-source statistical package) in a Malaysian public university. A descriptive quasi-experimental with control group design was employed. This study was conducted on 50 first year students enrolled in various nonmathematics/statistics oriented fields of study. The students were equally assigned to the experimental (used RStudio) and control (did not use RStudio) groups respectively. Descriptive analysis showed that experimental group students had higher students’ engagement than control group students. It also indicated that students’ engagement of experimental group students was high, while that of control group students was moderate. Independent samples t-tests revealed there were significant differences in all components of students’ engagement (behaviour engagement, emotion engagement, cognitive engagement and social engagement) and overall students’ engagement between students in both groups. Moreover, the mean differences between both groups were also statistically significant and favourable to experimental group. These findings implied that integration of RStudio exerted positive effects on students’ engagement in statistics classroom. Essentially, integration of RStudio in introductory statistics course has proven to be beneficial and significant to substantially improve students’ engagement to learn and to perform better in statistics course.

A Reliable Nomogram is Identified to Predict the Overall Survival for Patients with Esophageal Squamous Cancer after Chemoradiation Therapy followed by Surgery

Currently there is no reliable nomogram to predict the overall survival (OS) for patients with esophageal squamous cancer (ESCC) after chemoradiation therapy followed by surgery. The purpose of this study is to develop the above nomogram through the correlation of pretreatment clinical features that are commonly indicated for prognosis. We searched through Surveillance, Epidemiology, and End Results (SEER) database. A criterion for the search is listed as follows: (1) patients diagnosed with pathologically proved ESCC; (2) patients diagnosed in 2010-2014(AJCC 2010 7th version); (3) patients with T1-4N0-3M0 disease; (4) patients underwent chemoradiation therapy followed by surgery. Age, ethnicity, sex, Primary Site of the tumor, T stage, N stage were recorded as clinical predictors. These predictors were used in multivariable logistic regression analysis based nomograms to estimate the probabilities of OS. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. Analyses were performed with open-source statistical software R (version 3.2.1). There were 5,508 patients with ESCC diagnosed in 2010-2014, 326 patients were identified. A total of 41% of patients were male. A total of 62% were aged 50 to 70.72% and were in the white race. Proportion of the patients diagnosed in T1, T2, T3, and T4 were 15%, 18%, 63%, and 4%; 92% of the patients were in N0-1 stage; 88.3% of the tumor were located in middle and lower third of esophagus; only 4% had overlapping lesion of esophagus. The 3-year and 4-year OS for the entire cohort of the patients were 53%, 47% with the median survival time of 45 months. Median survival of male and female patients were 38 months vs. 54 months (p=0.024); 3 year survival of patients with white race and other race were 56% vs. 45%, p=0.050; 3 year OS for patients with T1-2 and T3-4 stage were 60.8% vs. 52.4%, p = 0.198. For patients with N0, N1, N2, and N3 stage, 3-year OS were 87.3%, 54.3%, 37.1%, and 0%, p < 0.001. Primary site of the tumor was not the prognostic factors. Nomograms were developed for the predicted OS with age, sex, ethnicity, T stage, and N stage. The calibration curve for probabilities showed relatively good agreement between prediction by nomogram and actual observation. The C-index of the nomograms for predicting for predicting OS is 0.63. The risk of having one of the indications of adjuvant radiation therapy increased with increases in predictors except for clinical T stage. Using clinical information, we produced nomograms, which may relatively accurately predict the probabilities of overall survival for patients with esophageal squamous cancer after chemoradiation therapy followed by surgery. This nomogram may help individualize initial treatment options.

Using Emulation to Engineer and Understand Simulations of Biological Systems.

Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understanding. These analyses may require repeated executions and extensive sampling of high-dimensional parameter spaces: analyses that may become intractable due to time and resource limitations. Significant reduction in these requirements can be obtained using surrogate models, or emulators, that can rapidly and accurately predict the output of an existing simulator. We apply emulation to evaluate and enrich understanding of a previously published agent-based simulator of lymphoid tissue organogenesis, showing an ensemble of machine learning techniques can reproduce results obtained using a suite of statistical analyses within seconds. This performance improvement permits incorporation of previously intractable analyses, including multi-objective optimization to obtain parameter sets that yield a desired response, and Approximate Bayesian Computation to assess parametric uncertainty. To facilitate exploitation of emulation in simulation-focused studies, we extend our open source statistical package, spartan, to provide a suite of tools for emulator development, validation, and application. Overcoming resource limitations permits enriched evaluation and refinement, easing translation of simulator insights into increased biological understanding.

Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP

BackgroundDespite its popularity as an inferential framework, classical null hypothesis significance testing (NHST) has several restrictions. Bayesian analysis can be used to complement NHST, however, this approach has been underutilized largely due to a dearth of accessible software options. JASP is a recently developed open-source statistical package that facilitates both Bayesian and NHST analysis using a graphical interface. This article provides an applied introduction to Bayesian inference with Bayes factors using JASP.MethodsWe use JASP to compare and contrast Bayesian alternatives for several common classical null hypothesis significance tests: correlations, frequency distributions, t-tests, ANCOVAs, and ANOVAs. These examples are also used to illustrate the strengths and limitations of both NHST and Bayesian hypothesis testing.ResultsA comparison of NHST and Bayesian inferential frameworks demonstrates that Bayes factors can complement p-values by providing additional information for hypothesis testing. Namely, Bayes factors can quantify relative evidence for both alternative and null hypotheses. Moreover, the magnitude of this evidence can be presented as an easy-to-interpret odds ratio.ConclusionsWhile Bayesian analysis is by no means a new method, this type of statistical inference has been largely inaccessible for most psychiatry researchers. JASP provides a straightforward means of performing reproducible Bayesian hypothesis tests using a graphical “point and click” environment that will be familiar to researchers conversant with other graphical statistical packages, such as SPSS.

2268 - Estimating the Determinants of Cycling: From Area to Road Network Levels

Background It is important to model the determinants of mode choice for many health-related reasons: to find-out what is holding people back from healthy active travel choices, to understand the drivers of detrimental car dependency, and to identify the causal factors over which public authorities have some control. Another motivation is that model results could feed into forecasts of behaviour change following specific interventions. Numerous papers have explored the issue but few have been able to provide results that can feed-into models forecasting uptake following local interventions for a number of reasons including small sample size, lack of a temporal component, and use of explanatory variables that cannot easily be applied beyond the case study region. This gap in the literature was identified and tackled as part of a UK Department for Transport funded project to develop a Cycling Infrastructure Prioritisation Toolkit (CyIPT). Methods Origin-destination (OD) data on commuter mode choice was obtained from the 2001 and 2011 Census. The data was processed to ensure that the 2001 data, which was provided at a different geographic level, as compatible with the 2011 data. It was further processed to create a dependent variable (cycling uptake) and explanatory variables including environmental (e.g. length of off-road cycle infrastructure) and demographic (e.g. age and gender splits) factors, for each desire-line. A model was developed to explain cycling uptake as a function of the explanatory variables and forecast future uptake following simulated interventions. Results Preliminary results show a clear infrastructure signal in the noise surrounding mode choice but other factors seem to be at play. Further research over the coming months will seek to improve the model and our resulting ability to forecast uptake at the local level from specific interventions. Conclusions Newly available data, combined with emerging high performance and open source statistical software, mean that large natural experiments on the impact of new infrastructure on mode choice can now be performed at the national level using geographically detailed data at the origin-destination level. This has the potential to make the prioritisation of new infrastructure much more evidence-based and holds the promise of allowing a-priori benefit-cost estimates to be automated to support transport planners to enable a rapid transition away from fossil fuels and towards healthy travel patterns across the world, in urban and rural areas alike.