pls regression models matlab r2018b Search Results


matlab r2013b  (MathWorks Inc)
96
MathWorks Inc matlab r2013b
Matlab R2013b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pls-da modeling calculations  (Eigenvector Research Inc)
90
Eigenvector Research Inc pls-da modeling calculations
Pls Da Modeling Calculations, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pls toolbox 8.7  (Eigenvector Research Inc)
90
Eigenvector Research Inc pls toolbox 8.7
The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.
Pls Toolbox 8.7, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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vip analyses  (MathWorks Inc)
93
MathWorks Inc vip analyses
The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.
Vip Analyses, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pls toolbox 7.8.2  (Eigenvector Research Inc)
90
Eigenvector Research Inc pls toolbox 7.8.2
The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.
Pls Toolbox 7.8.2, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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toolbox  (MathWorks Inc)
96
MathWorks Inc toolbox
The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.
Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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labspec 5  (HORIBA Ltd)
90
HORIBA Ltd labspec 5
The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.
Labspec 5, supplied by HORIBA Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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plsgui  (Baycrest Technology Pty Ltd)
90
Baycrest Technology Pty Ltd plsgui
The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.
Plsgui, supplied by Baycrest Technology Pty Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pls toolbox  (Baycrest Technology Pty Ltd)
90
Baycrest Technology Pty Ltd pls toolbox
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pls Toolbox, supplied by Baycrest Technology Pty Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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r version 3.0.1  (RStudio)
90
RStudio r version 3.0.1
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
R Version 3.0.1, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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the pls toolbox for matlab version 7.0.3  (Eigenvector Research Inc)
90
Eigenvector Research Inc the pls toolbox for matlab version 7.0.3
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
The Pls Toolbox For Matlab Version 7.0.3, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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hot plate  (Torrey Pines Scientific)
90
Torrey Pines Scientific hot plate
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Hot Plate, supplied by Torrey Pines Scientific, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.

Journal: Molecules

Article Title: Assessment of the Bulgarian Wastewater Treatment Plants’ Impact on the Receiving Water Bodies

doi: 10.3390/molecules24122274

Figure Lengend Snippet: The partial least squares–discriminant analysis (PLS-DA) model results for WWTP effluents and surface waters based on physicochemical water quality parameters: ( a ) Confusion matrix; ( b ) VIP (variable importance on projection) scores; ( c ) Regression vector for WWTP effluents; ( d ) Regression vector for surface waters.

Article Snippet: All PLS-DA modeling calculations were performed in MATLAB R2018b using PLS Toolbox 8.7 (Eigenvector Research Inc, Manson, WA, USA).

Techniques: Plasmid Preparation

Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Journal: Neurobiology of Stress

Article Title: Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

doi: 10.1016/j.ynstr.2023.100578

Figure Lengend Snippet: Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Article Snippet: To evaluate multivariate patterns of correlation between the nodal-level topological property (degree centrality) and SA alterations across subjects, we used PLSC via the publicly available PLS toolbox ( https://www.rotman-baycrest.on.ca/index.php?section=84 ) in MATLAB R2018b (MathWorks, Natick, USA).

Techniques: Functional Assay, Magnetic Resonance Imaging