Generalized linear models, with applications in fisheries research
- Authors: Sidumo, Bonelwa
- Date: 2018
- Subjects: Western mosquitofish , Analysis of variance , Fisheries Catch effort South Africa Sundays River (Eastern Cape) , Linear models (Statistics) , Multilevel models (Statistics) , Experimental design
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/61102 , vital:27975
- Description: Gambusia affinis (G. affinis) is an invasive fish species found in the Sundays River Valley of the Eastern Cape, South Africa, The relative abundance and population dynamics of G. affinis were quantified in five interconnected impoundments within the Sundays River Valley, This study utilised a G. affinis data set to demonstrate various, classical ANOVA models. Generalized linear models were used to standardize catch per unit effort (CPUE) estimates and to determine environmental variables which influenced the CPUE, Based on the generalized linear model results dam age, mean temperature, Oreochromis mossambicus abundance and Glossogobius callidus abundance had a significant effect on the G. affinis CPUE. The Albany Angling Association collected data during fishing tag and release events. These data were utilized to demonstrate repeated measures designs. Mixed-effects models provided a powerful and flexible tool for analyzing clustered data such as repeated measures data and nested data, lienee it has become tremendously popular as a framework for the analysis of bio-behavioral experiments. The results show that the mixed-effects methods proposed in this study are more efficient than those based on generalized linear models. These data were better modeled with mixed-effects models due to their flexibility in handling missing data.
- Full Text:
- Date Issued: 2018
- Authors: Sidumo, Bonelwa
- Date: 2018
- Subjects: Western mosquitofish , Analysis of variance , Fisheries Catch effort South Africa Sundays River (Eastern Cape) , Linear models (Statistics) , Multilevel models (Statistics) , Experimental design
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/61102 , vital:27975
- Description: Gambusia affinis (G. affinis) is an invasive fish species found in the Sundays River Valley of the Eastern Cape, South Africa, The relative abundance and population dynamics of G. affinis were quantified in five interconnected impoundments within the Sundays River Valley, This study utilised a G. affinis data set to demonstrate various, classical ANOVA models. Generalized linear models were used to standardize catch per unit effort (CPUE) estimates and to determine environmental variables which influenced the CPUE, Based on the generalized linear model results dam age, mean temperature, Oreochromis mossambicus abundance and Glossogobius callidus abundance had a significant effect on the G. affinis CPUE. The Albany Angling Association collected data during fishing tag and release events. These data were utilized to demonstrate repeated measures designs. Mixed-effects models provided a powerful and flexible tool for analyzing clustered data such as repeated measures data and nested data, lienee it has become tremendously popular as a framework for the analysis of bio-behavioral experiments. The results show that the mixed-effects methods proposed in this study are more efficient than those based on generalized linear models. These data were better modeled with mixed-effects models due to their flexibility in handling missing data.
- Full Text:
- Date Issued: 2018
Improved tree species discrimination at leaf level with hyperspectral data combining binary classifiers
- Authors: Dastile, Xolani Collen
- Date: 2011
- Subjects: Mathematical statistics , Analysis of variance , Nearest neighbor analysis (Statistics) , Trees--Classification
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5567 , http://hdl.handle.net/10962/d1002807 , Mathematical statistics , Analysis of variance , Nearest neighbor analysis (Statistics) , Trees--Classification
- Description: The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. However, binary classification works better. We constructed binary classifiers for all possible binary classification problems and combine them with Error Correcting Output Codes. We show especially that the use of 1-nearest neighbour binary classifiers results in no improvement compared to a direct 1-nearest neighbour 7-class predictor. In contrast to this negative result, the use of neural networks binary classifiers improves accuracy by 10% compared to a direct neural networks 7-class predictor, and error rates become acceptable. This can be further improved by choosing only suitable binary classifiers for combination.
- Full Text:
- Date Issued: 2011
- Authors: Dastile, Xolani Collen
- Date: 2011
- Subjects: Mathematical statistics , Analysis of variance , Nearest neighbor analysis (Statistics) , Trees--Classification
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5567 , http://hdl.handle.net/10962/d1002807 , Mathematical statistics , Analysis of variance , Nearest neighbor analysis (Statistics) , Trees--Classification
- Description: The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. However, binary classification works better. We constructed binary classifiers for all possible binary classification problems and combine them with Error Correcting Output Codes. We show especially that the use of 1-nearest neighbour binary classifiers results in no improvement compared to a direct 1-nearest neighbour 7-class predictor. In contrast to this negative result, the use of neural networks binary classifiers improves accuracy by 10% compared to a direct neural networks 7-class predictor, and error rates become acceptable. This can be further improved by choosing only suitable binary classifiers for combination.
- Full Text:
- Date Issued: 2011
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