pls regression matlab r2018a Search Results


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
https://www.bioz.com/result/pls-da modeling calculations/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls-da modeling calculations - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

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
https://www.bioz.com/result/pls toolbox 8.7/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls toolbox 8.7 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc pls-toolbox version 7.9.5
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.
Pls Toolbox Version 7.9.5, 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
https://www.bioz.com/result/pls-toolbox version 7.9.5/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls-toolbox version 7.9.5 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

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
https://www.bioz.com/result/labspec 5/product/HORIBA Ltd
Average 90 stars, based on 1 article reviews
labspec 5 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc pls tool box
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 Tool Box, 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
https://www.bioz.com/result/pls tool box/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls tool box - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc pls toolbox matlab version 8.6
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 Matlab Version 8.6, 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
https://www.bioz.com/result/pls toolbox matlab version 8.6/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls toolbox matlab version 8.6 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc pls toolbox chemometrics 112 software
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 Chemometrics 112 Software, 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
https://www.bioz.com/result/pls toolbox chemometrics 112 software/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls toolbox chemometrics 112 software - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc pls toolbox chemometrics software pls_toolbox_4.1
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 Chemometrics Software Pls Toolbox 4.1, 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
https://www.bioz.com/result/pls toolbox chemometrics software pls_toolbox_4.1/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
pls toolbox chemometrics software pls_toolbox_4.1 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

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
https://www.bioz.com/result/pls toolbox/product/Baycrest Technology Pty Ltd
Average 90 stars, based on 1 article reviews
pls toolbox - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Genedata Inc analyst version 8.1
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.
Analyst Version 8.1, supplied by Genedata Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/analyst version 8.1/product/Genedata Inc
Average 90 stars, based on 1 article reviews
analyst version 8.1 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc matlab r2019a add-on pls_toolbox 8.6.2
<t>Partial</t> <t>least</t> <t>square</t> discriminant analysis (PLS-DA) model based plot of the first three latent variables (LVs), LV1 (blue), LV2 (orange), and LV3 (yellow) of D1 (A) and D2 (B) resistant vs. susceptible model. Annotations indicate the centers of the peaks before the first derivative was taken. The dash line in the middle corresponds to 0 point. D1 = 1 day after treatment, and D2 = 2 days after treatment of glyphosate.
Matlab R2019a Add On Pls Toolbox 8.6.2, 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
https://www.bioz.com/result/matlab r2019a add-on pls_toolbox 8.6.2/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
matlab r2019a add-on pls_toolbox 8.6.2 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

90
Eigenvector Research Inc mcr-als gui 2.0
<t>Partial</t> <t>least</t> <t>square</t> discriminant analysis (PLS-DA) model based plot of the first three latent variables (LVs), LV1 (blue), LV2 (orange), and LV3 (yellow) of D1 (A) and D2 (B) resistant vs. susceptible model. Annotations indicate the centers of the peaks before the first derivative was taken. The dash line in the middle corresponds to 0 point. D1 = 1 day after treatment, and D2 = 2 days after treatment of glyphosate.
Mcr Als Gui 2.0, 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
https://www.bioz.com/result/mcr-als gui 2.0/product/Eigenvector Research Inc
Average 90 stars, based on 1 article reviews
mcr-als gui 2.0 - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

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

Partial least square discriminant analysis (PLS-DA) model based plot of the first three latent variables (LVs), LV1 (blue), LV2 (orange), and LV3 (yellow) of D1 (A) and D2 (B) resistant vs. susceptible model. Annotations indicate the centers of the peaks before the first derivative was taken. The dash line in the middle corresponds to 0 point. D1 = 1 day after treatment, and D2 = 2 days after treatment of glyphosate.

Journal: Frontiers in Plant Science

Article Title: Raman Spectroscopy Can Distinguish Glyphosate-Susceptible and -Resistant Palmer Amaranth ( Amaranthus palmeri )

doi: 10.3389/fpls.2021.657963

Figure Lengend Snippet: Partial least square discriminant analysis (PLS-DA) model based plot of the first three latent variables (LVs), LV1 (blue), LV2 (orange), and LV3 (yellow) of D1 (A) and D2 (B) resistant vs. susceptible model. Annotations indicate the centers of the peaks before the first derivative was taken. The dash line in the middle corresponds to 0 point. D1 = 1 day after treatment, and D2 = 2 days after treatment of glyphosate.

Article Snippet: The Raman spectra data were imported into the MATLAB R2019a add-on PLS_Toolbox 8.6.2 (Eigenvector Research Inc.) for statistical analyses.

Techniques: