The Distributions of the Actual Error Rates in Linear Discriminant Analysis

1980 ◽  
Vol 75 (369) ◽  
pp. 201-205 ◽  
Author(s):  
James W. Sayre
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Error Rates ◽  
Linear Discriminant ◽  
Actual Error

scholarly journals Song type variations of Louisiana Waterthrush (Parkesia motacilla) and their geographic distributions

2020 ◽  
Author(s):  
W. Ross Silcock ◽  
Shari L. Schwartz ◽  
John U. Carlini ◽  
Stephen J. Dinsmore
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Individual Variation ◽  
Large Scale ◽  
Error Rates ◽  
Western Hemisphere ◽  
Song Type ◽  
Linear Discriminant ◽  
Geographic Distributions ◽  
Song Types

AbstractLouisiana Waterthrush (Parkesia motacilla) is a familiar singer in the Western Hemisphere family Parulidae, yet apparent geographic variations in its song and potentially related causal mechanisms have not received detailed examination in previously published studies. Here, we analyzed song pattern variations of 651 Louisiana Waterthrush singers in audio spectrogram recordings obtained from our field work and publicly accessible bioacoustics archives. Visual and auditory assessment of the introductory note sequence of each song identified three distinct song types (A, B, and C) and 88.3% of the songs were assigned to one of these types. Linear Discriminant Analysis and Random Forest methods were used to verify the assignments and showed strong agreement (>90%) for Type A with slightly less agreement on Types B and C. User error rates (proportion of the Linear Discriminant Analysis classifications that were incorrect) were <10% for Types A and B, but 26% for Type C, while producer error rates (proportion of the song type for which the Linear Discriminant Analysis was incorrect) were >25% for Types A and C, but <5% for Type B. Our findings confirmed in a subset of 87 individuals that most between-individual variation was in the number of notes and note sequence duration while most within-individual variation resulted from the percent of downstrokes. The location of each singer was plotted on a map of the breeding range and results indicated the song types have large-scale discrete geographic distributions that co-occur in some regions but not range-wide. Evaluation of the distributions provided tentative support for a hypothesis that two of the song types may independently exhibit congruence with the geographic extent of Pleistocene glacial boundaries and the third song type may be distinguished by a lack of congruence, but further investigation is needed to elucidate whether the song variations represent subpopulations with three separate evolutionary histories.

scholarly journals Song type variations of Louisiana Waterthrush (Parkesia motacilla) and their geographic distributions

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256385
Author(s):  
W. Ross Silcock ◽  
Shari L. Schwartz ◽  
John U. Carlini ◽  
Stephen J. Dinsmore
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Individual Variation ◽  
Large Scale ◽  
Error Rates ◽  
Western Hemisphere ◽  
Song Type ◽  
Linear Discriminant ◽  
Geographic Distributions ◽  
Song Types

Louisiana Waterthrush (Parkesia motacilla) is a familiar singer in the Western Hemisphere family Parulidae, yet apparent geographic variations in its song and potentially related causal mechanisms have not received detailed examination in previously published studies. Here, we analyzed song pattern variations of 651 Louisiana Waterthrush singers in audio spectrogram recordings obtained from our field work and publicly accessible bioacoustics archives. Visual and auditory assessment of the introductory note sequence of each song identified three distinct song types (A, B, and C) and most of the songs were assigned to one of these types. Linear Discriminant Analysis and Random Forest methods were used to verify the assignments and showed strong agreement for Type A with slightly less agreement on Types B and C. User error rates (proportion of the Linear Discriminant Analysis classifications that were incorrect) were low for Types A and B, and somewhat higher for Type C, while producer error rates (proportion of the song type for which the Linear Discriminant Analysis was incorrect) were somewhat higher for Types A and C than the minimal levels achieved for Type B. Our findings confirmed that most between-individual variation was in the number of notes and note sequence duration while most within-individual variation resulted from the percent of downstrokes. The location of each singer was plotted on a map of the breeding range and results suggested the song types have large-scale discrete geographic distributions that co-occur in some regions but not range-wide. Evaluation of the distributions provided tentative support for a hypothesis that two of the song types may independently exhibit congruence with the geographic extent of Pleistocene glacial boundaries and the third song type may be distinguished by a lack of congruence, but further investigation is needed to elucidate whether the song variations represent subpopulations with three separate evolutionary histories.

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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