The Experts below are selected from a list of 203166 Experts worldwide ranked by ideXlab platform
Miguel B Araujo - One of the best experts on this subject based on the ideXlab platform.
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a comprehensive evaluation of Predictive Performance of 33 species distribution models at species and community levels
Ecological Monographs, 2019Co-Authors: Anna Norberg, Nerea Abrego, Guillaume F Blanchet, Frederick R Adler, Barbara J Anderson, Jani Anttila, Miguel B AraujoAbstract:A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the Predictive Performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their Performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure Predictive Performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their Predictive Performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model Performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary Performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study.
Mary Tate - One of the best experts on this subject based on the ideXlab platform.
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Assessing the Predictive Performance of structural equation model estimators
Journal of Business Research, 2016Co-Authors: Joerg Evermann, Mary TateAbstract:Structural equation models are traditionally used for theory testing. With the increasing importance of Predictive analytics, and the ability of structural equation models to maintain theoretical plausibility in the context of Predictive modeling, identifying how best to predict from structural equation models is important. Recent calls for a refocusing of partial least squares path modeling (PLSPM) on Predictive applications further increase the need to assess and compare the Predictive power of different estimation methods for structural equation models. This paper presents two simulation studies that evaluate the Performance of different modes and variations of PLSPM and covariance analysis on prediction from structural equation models. Study 1 examines all-reflective models using blindfolding and the Q2 statistic. Study 2 examines mixed formative-reflective models using out-of-sample cross-validation and the RMSE statistic. Recommendations to guide researchers in the choice of appropriate prediction method are offered.
Mindy M Syfert - One of the best experts on this subject based on the ideXlab platform.
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the effects of sampling bias and model complexity on the Predictive Performance of maxent species distribution models
PLOS ONE, 2013Co-Authors: Mindy M Syfert, Matthew J Smith, David A CoomesAbstract:Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model Performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as “feature types” in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on Predictive Performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.
Susan M Chapman - One of the best experts on this subject based on the ideXlab platform.
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the score matters wide variations in Predictive Performance of 18 paediatric track and trigger systems
Archives of Disease in Childhood, 2017Co-Authors: Susan M Chapman, Jo Wray, Kate Oulton, Christina Pagel, Samiran RayAbstract:Objective To compare the Predictive Performance of 18 paediatric early warning systems (PEWS) in predicting critical deterioration. Design Retrospective case-controlled study. PEWS values were calculated from existing clinical data, and the area under the receiver operator characteristic curve (AUROC) compared. Setting UK tertiary referral children9s hospital. Patients Patients without a ‘do not attempt resuscitation’ order admitted between 1 January 2011 and 31 December 2012. All patients on paediatric wards who suffered a critical deterioration event were designated ‘cases’ and matched with a control closest in age who was present on the same ward at the same time. Main outcome measures Respiratory and/or cardiac arrest, unplanned transfer to paediatric intensive care and/or unexpected death. Results 12 ‘scoring’ and 6 ‘trigger’ systems were suitable for comparative analysis. 297 case events in 224 patients were available for analysis. 244 control patients were identified for the 311 events. Three PEWS demonstrated better overall Predictive Performance with an AUROC of 0.87 or greater. Comparing each system with the highest performing PEWS with Bonferroni9s correction for multiple comparisons resulted in statistically significant differences for 13 systems. Trigger systems performed worse than scoring systems, occupying the six lowest places in the AUROC rankings. Conclusions There is considerable variation in the Performance of published PEWS, and as such the choice of PEWS has the potential to be clinically important. Trigger-based systems performed poorly overall, but it remains unclear what factors determine optimum Performance. More complex systems did not necessarily demonstrate improved Performance.
J B Glen - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of the Predictive Performance of four pharmacokinetic models for propofol
British journal of anaesthesia, 2009Co-Authors: J B Glen, F. ServinAbstract:Background This study has compared the Predictive Performance of four pharmacokinetic models, two of which are currently incorporated in commercial target-controlled infusion pumps for the administration of propofol. Methods Arterial propofol concentrations and patient characteristic data were available from nine patients who, in a published study, had received a standardized infusion of propofol. Predicted concentrations with ‘Diprifusor’ (Marsh), ‘Schnider’, ‘Schuttler’, and ‘White’ models were obtained by computer simulation. The Predictive Performance of each model was assessed overall and over the following phases: rapid infusion (1–5 min), early (1–21 min), maintenance (21-min end-infusion), and recovery (2–20 min post-infusion). Results The overall assessment, based on 29–36 samples from each patient, indicated that all four models were clinically acceptable. However, the negligible bias (−0.1%) with the ‘Schnider’ model was accompanied by overprediction in the rapid infusion phase and underprediction during recovery. This changing bias over time was not detected as ‘divergence’ when assessed on absolute Performance error (APE), (1.4% h−1) but became significant (13.2% h−1) when based on changes in signed PE over time. The ‘Schuttler’ model performed well at most phases but overpredicted concentrations during recovery. The White model led to a marginal improvement over ‘Diprifusor’ and would be expected to reduce the positive bias usually seen with ‘Diprifusor’ systems. Conclusions In assessing the Predictive Performance of pharmacokinetic models, additional information can be obtained by analysis of bias at different phases of an infusion. The evaluation of divergence should involve linear regression analysis of both absolute and signed PEs.
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Evaluation of the Predictive Performance of a 'Diprifusor' TCI system.
Anaesthesia, 1998Co-Authors: C F Swinhoe, J E Peacock, J B Glen, C S ReillyAbstract:The Predictive Performance of a 'Diprifusor' target controlled infusion system for propofol was examined in 46 patients undergoing major surgery, divided into three age groups (18-40, 41-55 and 56-80 years). Measured arterial propofol concentrations were compared with values calculated (predicted) by the target controlled infusion system. Performance indices (median Performance error and median absolute Performance error) were similar in the three age groups, with study medians of 16.2% and 24.1%, respectively. Mean values for 'divergence' and 'wobble' were -7.6%.h-1 and 21.9%, respectively. Measured concentrations tended to be higher than calculated concentrations, particularly following induction or an increase in target concentration. The mean (SD) propofol target concentration of 3.5 (0.7) micrograms.ml-1 during maintenance was lower in older patients, compared with higher target concentrations of 4.2 (0.6) and 4.3 (0.7) micrograms.ml-1 in the two younger age groups, respectively. The control of depth of anaesthesia was good in all patients and the Predictive Performance of the 'Diprifusor' target controlled infusion system was considered acceptable for clinical purposes.
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Evaluation of the Predictive Performance of a ‘Diprifusor’ TCI system
Anaesthesia, 1998Co-Authors: C F Swinhoe, J E Peacock, J B Glen, C S ReillyAbstract:The Predictive Performance of a 'Diprifusor' target controlled infusion system for propofol was examined in 46 patients undergoing major surgery, divided into three age groups (18-40, 41-55 and 56-80 years). Measured arterial propofol concentrations were compared with values calculated (predicted) by the target controlled infusion system. Performance indices (median Performance error and median absolute Performance error) were similar in the three age groups, with study medians of 16.2% and 24.1%, respectively. Mean values for 'divergence' and 'wobble' were -7.6%.h-1 and 21.9%, respectively. Measured concentrations tended to be higher than calculated concentrations, particularly following induction or an increase in target concentration. The mean (SD) propofol target concentration of 3.5 (0.7) micrograms.ml-1 during maintenance was lower in older patients, compared with higher target concentrations of 4.2 (0.6) and 4.3 (0.7) micrograms.ml-1 in the two younger age groups, respectively. The control of depth of anaesthesia was good in all patients and the Predictive Performance of the 'Diprifusor' target controlled infusion system was considered acceptable for clinical purposes.
