Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

BACKGROUND: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. RESULTS: We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. CONCLUSION: Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives

Mathematical modelling of the growth of an Antarctic bacterium Rhodococcus sp. strain ADL36 on palm oil

The Ninth Symposium on Polar Science/Ordinary sessions: [OB] Polar biology, Wed. 5 Dec. / Entrance Hall (1st floor), National Institute of Polar Researc

LOAD-VELOCITY SLOPE CAN BE AN INDICATOR OF THE ACTIVE DRAG IN FRONT CRAWL SWIMMING

The purpose of this study was to investigate the relationship between swimming load-velocity slope and the active drag (Da) in front crawl. 19 female and 22 male swimmers were recruited and performed three 25 m front crawl sprints with different external loads (1, 3, 5 kg for females and 1, 5, and 9 kg for males) assigned by a robotic resistance device. The mean swimming velocity was plotted against the external load to establish the load-velocity profile for each swimmer. Da was obtained by the velocity perturbation method. The relationship between the load-velocity slope and Da was assessed using the Pearson correlation coefficient, which showed a very large correlation (r = 0.84, p \u3c 0.001) and an extremely large correlation (r = 0.93, p \u3c 0.01) for female and male swimmers, respectively, indicating that the load-velocity slope is an indicator of Da in front crawl swimming

Correlating Antiagglomerant Performance with Gas Hydrate Cohesion

Although inhibiting hydrate formation in hydrocarbon–water systems is paramount in preventing pipe blockage in hydrocarbon transport systems, the molecular mechanisms responsible for antiagglomerant (AA) performance are not completely understood. To better understand why macroscopic performance is affected by apparently small changes in the AA molecular structure, we perform molecular dynamics simulations. We quantify the cohesion energy between two gas hydrate nanoparticles dispersed in liquid hydrocarbons in the presence of different AAs, and we achieve excellent agreement against experimental data obtained at high pressure using the micromechanical force apparatus. This suggests that the proposed simulation approach could provide a screening method for predicting, in silico, the performance of new molecules designed to manage hydrates in flow assurance. Our results suggest that entropy and free energy of solvation of AAs, combined in some cases with the molecular orientation at hydrate–oil interfaces, are descriptors that could be used to predict performance, should the results presented here be reproduced for other systems as well. These insights could help speed up the design of new AAs and guide future experiments

Descriptive profile for lower-limb range of motion in professional road cyclists.

BACKGROUND: To describe the lower limb range of motion (ROM) profile in professional road cyclists. METHODS: Cohort study. One hundred and twenty-one road cyclists volunteered to participate. ROM measurements of passive hip flexion, extension, internal rotation, external rotation, knee flexion and ankle dorsiflexion in dominant and non-dominant limbs were performed using an inclinometer. ROM scores were individually categorized as normal or restricted according to reference values. RESULTS: Overall, hip flexion was smaller in the non-dominant limb than in the dominant limb (F=12.429, P<0.001), with bilateral differences in male (95% mean diff: 0.5° to 3.3°) and female cyclists (95% mean diff: 0.1° to 3.1°). Sex differences were found in hip flexion (F=18.346, P<0.001), hip internal rotation (F=6.030, P=0.016) and ankle dorsiflexion (F=4.363, P=0.039), with males showing smaller ROM than females. Males and females had restricted knee flexion in dominant (males: 51.6%; females: 42.6%) and non-dominant limbs (males: 45.0%; females: 39.3%). Ankle dorsiflexion was also restricted in dominant (males: 38.3%; females: 31.1%) and non-dominant limbs (males: 41.6%; females: 34.4%). CONCLUSIONS: Elite road cyclists showed restricted lower-limb ROM according to reference values. In general, male cyclists showed lower values of ROM than females’ counterparts. These findings suggest that including specific stretching exercises and resistance training to improve knee and ankle dorsiflexion ROM may prevent muscle imbalances caused by chronic pedaling in professional cyclists.pre-print1080 K

Detection of Towns Having a Peculiarity by Using Regression Models

This paper proposes a method to detect towns having a peculiarity, which is a statistical outlier from a statistical table. A statistic often contains data that are peculiar and are also known as outliers which are followed as large residuals in regression models. The detection of outliers in statistical tables was studied. The table has 22 explanatory variables, one response variable and 1947 records which can clarify their efficient causes or mixed effects. This information have greatly helped local governments with their policy and improvement of each region, for example; infrastructures, public services, and subsidies or grants. Although many studies have been made on grouping records or building a predictive model to overcome outliers, little attention has been given to find outliers. Many of those studies require a model’s parameter tuning and learning, or a description of a fitting function. Furthermore, for municipal officers to find outliers, it would be desirable to be able to analyze readily Free Software R without programming. Therefore, we propose a method to detect outlier from a statistical table by using three regression models which do not require learning and parameter adjustment provided by R

Application of Outlier Robust Nonlinear Mixed Effect Estimation in Examining the Effect of Phenylephrine in Rat Corpus Cavernosum

Abstract Ignoring two main characteristics of the concentration-response data, correlation between observations and presence of outliers, may lead to misleading results. Therefore, the special method should be considered. The present study was designed to apply the outlier robust nonlinear mixed estimation for effects of phenylephrine on rat corpus cavernosum strips. In this study, eight different doses of phenylephrine in eight experimental groups were used. Each group consisted of eight rats. The concentration-response curves to phenylephrine (0.1µM to 300µM) were obtained by the cumulative addition of phenylephrine to the chamber. Because of the existence of an outlier to achieve robust estimations, M-estimation method and Huber function as a dispersion function were used. Cumulative administration of phenylephrine (0.1µM -300µM) caused concentration-dependent contractions in strips of rat corpus cavernosum (-Log EC 50 was 5 ± 0.31, 95% CI= 5.92 to 4.21). The contraction of corpus cavernosum started in the concentration of 0.3 μM and then gradually increased in a dose-dependent manner till it reached a plateau in 100 μM. To consider the clustering feature of concentration-response data, the 4pl regression with a random term has been used. To estimate parameters, because of existence of an outlier in dataset, the robust procedure has been applied. The contraction of corpus cavernosum started in the concentration of 0.3 μM and then gradually increased in a dose-dependent manner till it reached a plateau in 100 μM

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate-21

<p><b>Copyright information:</b></p><p>Taken from "Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate"</p><p>BMC Bioinformatics 2006;7():123-123.</p><p>Published online 9 Mar 2006</p><p>PMCID:PMC1472692.</p><p>Copyright © 2006 Motulsky and Brown; licensee BioMed Central Ltd.</p

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate-1

<p><b>Copyright information:</b></p><p>Taken from "Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate"</p><p>BMC Bioinformatics 2006;7():123-123.</p><p>Published online 9 Mar 2006</p><p>PMCID:PMC1472692.</p><p>Copyright © 2006 Motulsky and Brown; licensee BioMed Central Ltd.</p