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pls function plsregress.m  (MathWorks Inc)


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    MathWorks Inc pls function plsregress.m
    The prediction of chronological age <t>with</t> <t>EEG‐age</t> via <t>PLS</t> analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).
    Pls Function Plsregress.M, supplied by MathWorks 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/product/plsregress%2Em/pmc11911306-213-24-25
    Average 90 stars, based on 1 article reviews
    pls function plsregress.m - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning"

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    Journal: Psychophysiology

    doi: 10.1111/psyp.70033

    The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).
    Figure Legend Snippet: The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).

    Techniques Used:

    The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.
    Figure Legend Snippet: The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.

    Techniques Used:

    Differences between correlations of chronological age with brain age for PAF age and  PLS   EEG  age methods.
    Figure Legend Snippet: Differences between correlations of chronological age with brain age for PAF age and PLS EEG age methods.

    Techniques Used:

    Correlations of M‐PAF age,  PLS   EEG  age, and chronological age (CA) with QMCI.
    Figure Legend Snippet: Correlations of M‐PAF age, PLS EEG age, and chronological age (CA) with QMCI.

    Techniques Used:

    Differences between correlations of age with QMCI for M‐PAF age,  PLS   EEG  age, and chronological age (CA), when accounting for NART‐IQ.
    Figure Legend Snippet: Differences between correlations of age with QMCI for M‐PAF age, PLS EEG age, and chronological age (CA), when accounting for NART‐IQ.

    Techniques Used:

    Related Articles

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    Article Title: Acoustic and Categorical Dissimilarity of Musical Timbre: Evidence from Asymmetries Between Acoustic and Chimeric Sounds
    Article Snippet: Here we use PLSR as implemented in the plsregress.m function provided by MATLAB version R2013a (The MathWorks, Inc., Natick, MA), which applies the SIMPLS algorithm (De Jong, ).

    Article Title: Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models
    Article Snippet: In the PLSR version used in the present paper (SIMPLS [21], with the function "plsregress.m" in MATLAB ® [22] Statistics ToolboxTM v7.6), each of the auxillary Y-score vectors ty,a, originally obtained by eq.

    Article Title: Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models
    Article Snippet: The MATLAB ® [ ] function "plsregress.m" (from the Statistics ToolboxTM v7.2) was used for the PLSR both for the global and the local regression analyses.

    Article Title: Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features
    Article Snippet: PLS regression was calculated using the Matlab build-in function plsregress.m .

    Article Title: Artificial neural network-based multi-input multi-output model for short-term storm surge prediction on the southeast coast of China
    Article Snippet: In recent years, to reduce social and economic losses, timely and accurate storm surge forecasts have been attracting growing attention from coastal engineers.. Although a host of studies have demonstrated the feasibility of artificial neural networks (ANNs) in predicting storm surges, few elaborated parametric studies have been performed to investigate the optimal sliding window sizes of input variables for the ANN models, and the effect of the selection of training data, particularly concerning typhoon intensity and tracks, on model performance remained less understood.. This work proposes a multi-input and multi-output (MIMO) neural network to forecast storm surge time series along the southeast coast of China (SCC).

    Article Title: The evolution and sensitivity of katabatic flow dynamics to external influences through the evening transition
    Article Snippet: Data collected over an arid shallow slope (2–4◦) during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program are used to study the katabatic structure and onset of katabatic flow through the evening transition.. An unprecedented suite of instrumentation, including a transect of five turbulence towers with 29 sonic anemometers, is used for the investigation.. Fifteen transition periods with well-defined katabatic flow and relatively little synoptic forcing are used in the study.



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    The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    Differences between correlations of chronological age with brain age for PAF age and  PLS   EEG  age methods.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: Differences between correlations of chronological age with brain age for PAF age and PLS EEG age methods.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    Correlations of M‐PAF age,  PLS   EEG  age, and chronological age (CA) with QMCI.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: Correlations of M‐PAF age, PLS EEG age, and chronological age (CA) with QMCI.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    Differences between correlations of age with QMCI for M‐PAF age,  PLS   EEG  age, and chronological age (CA), when accounting for NART‐IQ.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: Differences between correlations of age with QMCI for M‐PAF age, PLS EEG age, and chronological age (CA), when accounting for NART‐IQ.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques: