Performance Evaluation of Logistic Regression, Linear Discriminant Analysis, and Classification and Regression Trees Under Controlled Conditions

Logistic Regression (LR), Linear Discriminant Analysis (LDA), and Classification and Regression Trees (CART) are common classification techniques for prediction of group membership. Since these methods are applied for similar purposes with different procedures, it is important to evaluate the performance of these methods under different controlled conditions. With this information in hand, researchers can apply the optimal method for certain conditions. Following previous research which reported the effects of conditions such as sample size, homogeneity of variancecovariance matrices, effect size, and predictor distributions, this research focused on effects of correlation between predictor variables, number of the predictor variables, number of the groups in the outcome variable, and group size ratios for the performance of LDA, LR, and CART. Data were simulated with Monte Carlo procedures in R statistical software and a factorial ANOVA with follow-ups was employed to evaluate the effect of conditions on the performance of each technique as measured by proportions of correctly predicted observations for all groups and for the smallest group. In most of the conditions for the two outcome measures, higher performances of CART than LDA and LR were observed. But, in some conditions where there were a higher number of predictor variables and number of groups with low predictor variable correlation, superiority of LR to CART was observed. Meaningful effects of methods of correlation, number or predictor variables, group numbers and group size ratio were observed on prediction accuracy of group membership. Effects of correlation, group size ratio, group number, and number of predictor variables on prediction accuracies were higher for LDA and LR than CART. For the three methods, lower correlation and greater number of predictor variables yielded higher prediction accuracies. Having balanced data rather than imbalanced data and greater group numbers led to lower group membership prediction accuracies for all groups, but having more groups led to better predictions for the small group. In general, based on these results, researchers are encouraged to apply CART in most conditions except for the cases when there are many predictor variables (around 10 or more) and non-binary groups with low correlations between predictor variables, when LR might provide more accurate results

Dinosaur micro-remains from the Middle Jurassic of Britain

In the Middle Jurassic, Great Britain was situated at ~30° north in an area of shallow seas with surrounding low-lying landmasses. Fluctuations in relative sea level resulted in emergent areas preserving snapshots of the terrestrial fauna in microvertebrate sites throughout southern England. Analysis of the dinosaur material, mostly isolated teeth, has resulted in a much more granular view of the taxa present including clades previously unknown or unconfirmed from this period. I developed machine learning techniques, which combined with morphological-based approaches confirms the presence of at least three maniraptoran taxa in the assemblage: three dromaeosaur morphotypes; a troodontid; and a therizinosaur. These results provide the first quantitative support for the presence of maniraptoran theropods, including the oldest occurrences of troodontids and therizinosaurs worldwide, in the Middle Jurassic and are consistent with predictions made by phylogenetic analyses. There are at least six ornithischian taxa in the assemblage; a distinctive highly-ridged morphotype that cannot be referred with certainty to any known ornithischian taxa and therefore represents a new taxon; a number of small teeth with denticles restricted to the upper third of the crown which represent a hitherto unknown occurrence of heterodontosaurids in the Middle Jurassic of the UK; at least one morphotype of a basal thyreophoran; an indeterminate thyreophoran; a stegosaur, which represents one of the oldest stegosaurs worldwide; and a number of ankylosaur morphotypes which make up the vast majority of the isolated ornithischian teeth seen from these sites. The application of machine learning, when combined with traditional morphological comparisons provides a powerful tool for the qualitative assessment of isolated teeth. This analysis increases the known diversity of Middle Jurassic dinosaur taxa in the UK and the confirmation of early maniraptorans, heterodontosaurids and stegosaurs highlights the importance of incorporating microvertebrate remains into faunal and evolutionary analyses

An in vivo investigation of optic nerve head microstructure in primary open angle glaucoma

Glaucoma remains the leading cause of irreversible blindness in the world. Since retinal ganglion cell (RGC) axonal degeneration precedes permanent vision loss, identification of ONH parameters affected in the earliest stages of primary open angle glaucoma (POAG) is critical to ensure early diagnosis. This cross-sectional study used enhanced-depth imaging optical coherence tomography (EDI-OCT; 1040/70nm) to acquire 10° and 20° scans centred on the ONH (glaucomatous; n=128 or healthy controls; n=60). Regional measures of prelamina and LC depth and thickness, nerve fibre layer thickness at ONH border (bNFL) and peripapillary (pNFL), neuroretinal minimum rim width; (MRW) and area; (MRA) were analysed. This is the first study to quantify volumetric parameters including optic cup, prelamina and LC volume, and also Bruch’s membrane opening (BMO) surface area. Furthermore, LC connective tissue alignment was probed regionally and depth-wise within the LC. Statistical modelling was performed to identify ONH parameters that best contributed to characterisation of ONHs in the earliest stages of POAG. Regional measures of prelamina depth and thickness, and LC thickness were able to differentiate between control eyes and preperimetric (PG), and early glaucoma (EG) (P<0.05). Additionally, EG LC volume was significantly less than in controls (P<0.05). Significant associations of these parameters with loss of VF sensitivity (VF Mean deviation [MD]) were identified. Border and pNFL thickness, MRW (but not MRA) significantly differed between controls and PG and EG (P<0.05); and decreased with VF MD. Lamina cribrosa connective tissue alignment altered in a region and depth specific manner between PG LC and controls, or EG LCs (P<0.05), providing an original in vivo indicator of disease. In conclusion, in vivo ONH and NFL parameters are able to discriminate between healthy ONHs and early POAG ONHs; providing a group index with potential as a novel biomarker for early diagnosis, critical to personalised clinical decision making