2022
DOI: 10.1007/s00357-022-09423-x
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Multinomial Principal Component Logistic Regression on Shape Data

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Cited by 6 publications
(2 citation statements)
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“…We computed Multinomial Principal Component Logistic Regression (MLR) models (Moghimbeygi & Nodehi, 2022), in order to estimate the morphological affinities of the specimens of our study group with the groups of the comparative sample. As a multinomial logistic regression, MLR is used to classify several possible outcomes, that is, the different groups of our comparative sample, using predictor variables, that is, the PC scores and the residuals from the linear regression of the PC scores on the natural logarithm of the centroid size of the specimens.…”
Section: Discussionmentioning
confidence: 99%
“…We computed Multinomial Principal Component Logistic Regression (MLR) models (Moghimbeygi & Nodehi, 2022), in order to estimate the morphological affinities of the specimens of our study group with the groups of the comparative sample. As a multinomial logistic regression, MLR is used to classify several possible outcomes, that is, the different groups of our comparative sample, using predictor variables, that is, the PC scores and the residuals from the linear regression of the PC scores on the natural logarithm of the centroid size of the specimens.…”
Section: Discussionmentioning
confidence: 99%
“…(2020), Dadakas and Tatsi (2021), Heng et al . (2023), Moghimbeygi and Nodehi (2022), Trinh et al . (2021) and Zhang et al.…”
Section: Methodology and Datamentioning
confidence: 99%