Performance evaluation of four-parameter models of the soil-water characteristic curve.
Ontology highlight
ABSTRACT:
SUBMITTER: Matlan SJ
PROVIDER: S-EPMC4055110 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Accurate estimation of soil water retention curve (SWRC) at the dry region is required to describe the relation between soil water content and matric suction from saturation to oven dryness. In this study, the extrapolative capability of two models for predicting the complete SWRC from limited ranges of soil water retention data was evaluated. When the model parameters were obtained from SWRC data in the 0-1500 kPa range, the FX model (Fredlund and Xing, 1994) estimations agreed well with measurements from saturation to oven dryness with RMSEs less than 0.01. The GG model (Groenevelt and Grant, 2004) produced larger errors at the dry region, with significantly larger RMSEs and MEs than the FX model. Further evaluations indicated that when SWRC measurements in the 0-100 kPa suction range was applied for model establishment, the FX model was capable of producing acceptable SWRCs across the entire water content range. For a higher accuracy, the FX model requires soil water retention data at least in the 0- to 300-kPa range to extend the SWRC to oven dryness. Comparing with the Khlosi et al. (2006) model, which requires measurements in the 0-500 kPa range to reproduce the complete SWRCs, the FX model has the advantage of requiring less SWRC measurements. Thus the FX modeling approach has the potential to eliminate the processes for measuring soil water retention in the dry range.
| S-EPMC4252034 | biostudies-literature
Project description:This study compares the kernel equating (KE) and test characteristic curve (TCC) equating methods using the nonequivalent anchor test equating design. In this Monte Carlo study, four independent variables were examined: sample size, test length, average form discrimination, anchor test reliability, and the percentage of anchor items. For each condition, there were 100 replications. To assess the performance of TCC equating and KE, the differences between the examinee parametric true scores and the equated estimated expected true scores were examined. The equated scores were based on the average across replications for each condition. Generally speaking, both KE and TCC equating produced accurate results, although KE tended to perform better than TCC on the parametric true score scale across conditions. Past research and the current study's results seem to indicate that KE should be strongly considered for most equating situations, particularly in light of its flexibility.
| S-EPMC5978648 | biostudies-literature
Project description:ObjectivesThe area under a receiver operating characteristic (ROC) curve (AUC) is a popular measure of pure diagnostic accuracy that is independent from the proportion of diseased subjects in the analysed sample. However, its actual usefulness in the clinical context has been questioned, because it does not seem to be directly related to the actual performance of a diagnostic marker in identifying diseased and non-diseased subjects in real clinical settings. This study evaluates the relationship between the AUC and the proportion of correct classifications (global diagnostic accuracy, GDA) in relation to the shape of the corresponding ROC curves.MethodsWe demonstrate that AUC represents an upward-biased measure of GDA at an optimal accuracy cut-off for balanced groups. The magnitude of bias depends on the shape of the ROC plot and on the proportion of diseased and non-diseased subjects. In proper curves, the bias is independent from the diseased/non-diseased ratio and can be easily estimated and removed. Moreover, a comparison between 2 partial AUCs can be replaced by a more powerful test for the corresponding whole AUCs.ResultsApplications to 3 real datasets are provided: a marker for a hormone deficit in children, 2 tumour markers for malignant mesothelioma, and 2 gene expression profiles in ovarian cancer patients.ConclusionsThe AUC is a measure of accuracy with potential clinical relevance for the evaluation of disease markers. The clinical meaning of ROC parameters should always be evaluated with an analysis of the shape of the corresponding ROC curve.
| S-EPMC10089712 | biostudies-literature
Project description:Current methods for fitting integrated rate equations to enzyme progress curves treat each observation as if it were an independent measurement. When the data are obtained by taking several successive readings from each of a series of progress curves, the data will not be truly independent and will exhibit autocorrelation. Here we propose a simple pragmatic extension of integrated rate equations which takes account of first-order autocorrelations. The value of the method is assessed when applied to five sets of experimental data.
| S-EPMC1135537 | biostudies-other
Project description:Both the four-parameter logistic (4PL) and the five-parameter logistic (5PL) models are widely used in nonlinear calibration. In this paper, we study the choice between 5PL and 4PL both by the accuracy and precision of the estimated concentrations and by the power to detect an association between a binary disease outcome and the estimated concentrations. Our results show that when the true curve is symmetric around its inflection point, the efficiency loss from using 5PL is negligible under the prevalent experimental design. When the true curve is asymmetric, 4PL may sometimes offer better performance due to bias-variance trade-off. We provide a practical guideline for choosing between 5PL and 4PL and illustrate its application with a real dataset from the HIV Vaccine Trials Network laboratory.
| S-EPMC4263697 | biostudies-literature
Project description:The objective of this study is to estimate multiple-cycles of the soil-water characteristic curve (SWCC) using an innovative volumetric pressure plate extractor (VPPE), which is incorporated with a membrane and time domain reflectometry (TDR). The pressure cell includes the membrane to reduce the experimental time and the TDR probe to automatically estimate the volumetric water content. For the estimation of SWCC using the VPPE system, four specimens with different grain size and void ratio are prepared. The volumetric water contents of the specimens according to the matric suction are measured by the burette system and are estimated in the TDR system during five cycles of SWCC tests. The volumetric water contents estimated by the TDR system are almost identical to those determined by the burette system. The experimental time significantly decreases with the new VPPE. The hysteresis in the SWCC is largest in the first cycle and is nearly identical after 1.5 cycles. As the initial void ratio decreases, the air entry value increases. This study suggests that the new VPPE may effectively estimate multiple-cycles of the SWCC of unsaturated soils.
| S-EPMC5456963 | biostudies-other
Project description:Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.
| S-EPMC9199579 | biostudies-literature
Project description:Modelling the growth curves of animals is important for optimizing the management and efficiency of animal production; however, little is known about the growth curves in American mink (Neovison vison). The study evaluated the performances of four three-parameter (Logistic, Gompertz, von Bertalanffy, and Brody), four four-parameter (Richards, Weibull, Bridges, and Janoscheck) and two polynomial models for describing the growth curves in mink. Body weights were collected from the third week of life to the week 31 in 738 black mink (373 males and 365 females). Models were fitted using the nls and nlsLM functions in stats and minpack.lm packages in R software, respectively. The Akaike's information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, Logistic and Richards were the best models for males and females, respectively. Four-parameter models had better performance compared to the other models, except Logistic model. The estimated maximum weight and mature growth rate varied among the models and differed between males and females. The results indicated that males and females had different growth curves as males grew faster and reached to the maximum body weight later compared to females. Further studies on genetic parameters and selection response for growth curve parameters are required for development of selection programs based on the shape of growth curves in mink.
| S-EPMC7023449 | biostudies-literature
Project description:Time-dependent receiver operating characteristic curves are often used to evaluate the classification performance of continuous measures when considering time-to-event data. When one is interested in evaluating the predictive performance of multiple covariates, it is common to use the Cox proportional hazards model to obtain risk scores; however, previous work has shown that when the model is mis-specified, the estimand corresponding to the partial likelihood estimator depends on the censoring distribution. In this manuscript, we show that when the risk score model is mis-specified, the AUC will also depend on the censoring distribution, leading to either over- or under-estimation of the risk score's predictive performance. We propose the use of censoring-robust estimators to remove the dependence on the censoring distribution and provide empirical results supporting the use of censoring-robust risk scores.
| S-EPMC8671363 | biostudies-literature
Project description:BackgroundLogistic regression-based signal detection algorithms have benefits over disproportionality analysis due to their ability to handle potential confounders and masking factors. Feature exploration and developing alternative machine learning algorithms can further strengthen signal detection.ObjectivesOur objective was to compare the signal detection performance of logistic regression, gradient-boosted trees, random forest and support vector machine models utilizing Food and Drug Administration adverse event reporting system data.DesignCross-sectional study.MethodsThe quarterly data extract files from 1 October 2017 through 31 December 2020 were downloaded. Due to an imbalanced outcome, two training sets were used: one stratified on the outcome variable and another using Synthetic Minority Oversampling Technique (SMOTE). A crude model and a model with tuned hyperparameters were developed for each algorithm. Model performance was compared against a reference set using accuracy, precision, F1 score, recall, the receiver operating characteristic area under the curve (ROCAUC), and the precision-recall curve area under the curve (PRCAUC).ResultsModels trained on the balanced training set had higher accuracy, F1 score and recall compared to models trained on the SMOTE training set. When using the balanced training set, logistic regression, gradient-boosted trees, random forest and support vector machine models obtained similar performance evaluation metrics. The gradient-boosted trees hyperparameter tuned model had the highest ROCAUC (0.646) and the random forest crude model had the highest PRCAUC (0.839) when using the balanced training set.ConclusionAll models trained on the balanced training set performed similarly. Logistic regression models had higher accuracy, precision and recall. Logistic regression, random forest and gradient-boosted trees hyperparameter tuned models had a PRCAUC ⩾ 0.8. All models had an ROCAUC ⩾ 0.5. Including both disproportionality analysis results and additional case report information in models resulted in higher performance evaluation metrics than disproportionality analysis alone.
| S-EPMC10752114 | biostudies-literature