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generalized linear mixed-effects model using matlab function fitglme  (MathWorks Inc)


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    MathWorks Inc generalized linear mixed-effects model using matlab function fitglme
    Generalized Linear Mixed Effects Model Using Matlab Function Fitglme, 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/result/generalized linear mixed-effects model using matlab function fitglme/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    generalized linear mixed-effects model using matlab function fitglme - by Bioz Stars, 2026-03
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    generalized linear mixed-effects model using matlab function fitglme  (MathWorks Inc)
    90
    MathWorks Inc generalized linear mixed-effects model using matlab function fitglme
    Generalized Linear Mixed Effects Model Using Matlab Function Fitglme, 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/result/generalized linear mixed-effects model using matlab function fitglme/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    generalized linear mixed-effects model using matlab function fitglme - by Bioz Stars, 2026-03
    90/100 stars
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    general linear mixed effects models using matlab's fitglme  (MathWorks Inc)
    90
    MathWorks Inc general linear mixed effects models using matlab's fitglme
    Effect sizes from a <t>general</t> <t>linear</t> <t>mixed</t> model (see “Methods”) estimating impact of stimulus category ( a ), choice ( b – d ), and action ( e – h ) on MEG power, in different regions and frequency bands. a Effect of current stimulus category on gamma (top) and alpha (bottom) power during test stimulus ( n = 60). b As a , but for effect of previous choice in the reference interval. c As b , but for test stimulus interval. d Comparison of choice history signals between alternators ( n = 25; orange) and repeaters ( n = 34; purple). Group effect = 0.55328, CI [0.19342, 0.91314], p = 0.00258. e Effect of current action preparation on gamma (top) and beta (bottom) power during test stimulus ( n = 60). f As e , but for effect of previous action during reference interval. g As f , but for the test interval. h Comparison of action history signals between alternators ( n = 25; orange) and repeaters ( n = 34; purple). Data are shown as average fixed effect size +/− 95% confidence intervals; *0.01 < p < 0.05 (no such effect present); **0.001 < p < 0.01; *** p < 0.001; filled markers, p < 0.05 (all p values FDR corrected).
    General Linear Mixed Effects Models Using Matlab's Fitglme, 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/result/general linear mixed effects models using matlab's fitglme/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    general linear mixed effects models using matlab's fitglme - by Bioz Stars, 2026-03
    90/100 stars
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    Effect sizes from a general linear mixed model (see “Methods”) estimating impact of stimulus category ( a ), choice ( b – d ), and action ( e – h ) on MEG power, in different regions and frequency bands. a Effect of current stimulus category on gamma (top) and alpha (bottom) power during test stimulus ( n = 60). b As a , but for effect of previous choice in the reference interval. c As b , but for test stimulus interval. d Comparison of choice history signals between alternators ( n = 25; orange) and repeaters ( n = 34; purple). Group effect = 0.55328, CI [0.19342, 0.91314], p = 0.00258. e Effect of current action preparation on gamma (top) and beta (bottom) power during test stimulus ( n = 60). f As e , but for effect of previous action during reference interval. g As f , but for the test interval. h Comparison of action history signals between alternators ( n = 25; orange) and repeaters ( n = 34; purple). Data are shown as average fixed effect size +/− 95% confidence intervals; *0.01 < p < 0.05 (no such effect present); **0.001 < p < 0.01; *** p < 0.001; filled markers, p < 0.05 (all p values FDR corrected).

    Journal: Nature Communications

    Article Title: Persistent activity in human parietal cortex mediates perceptual choice repetition bias

    doi: 10.1038/s41467-022-33237-5

    Figure Lengend Snippet: Effect sizes from a general linear mixed model (see “Methods”) estimating impact of stimulus category ( a ), choice ( b – d ), and action ( e – h ) on MEG power, in different regions and frequency bands. a Effect of current stimulus category on gamma (top) and alpha (bottom) power during test stimulus ( n = 60). b As a , but for effect of previous choice in the reference interval. c As b , but for test stimulus interval. d Comparison of choice history signals between alternators ( n = 25; orange) and repeaters ( n = 34; purple). Group effect = 0.55328, CI [0.19342, 0.91314], p = 0.00258. e Effect of current action preparation on gamma (top) and beta (bottom) power during test stimulus ( n = 60). f As e , but for effect of previous action during reference interval. g As f , but for the test interval. h Comparison of action history signals between alternators ( n = 25; orange) and repeaters ( n = 34; purple). Data are shown as average fixed effect size +/− 95% confidence intervals; *0.01 < p < 0.05 (no such effect present); **0.001 < p < 0.01; *** p < 0.001; filled markers, p < 0.05 (all p values FDR corrected).

    Article Snippet: We used general linear mixed effects models (GLMEs, using Matlab’s fitglme ) to quantify the effect of choice history on single-trial power modulation values across all source-level ROIs, frequency ranges and the above-defined time windows.

    Techniques: Comparison

    Time-resolved effect sizes from a general linear mixed model (see “Methods”), separately for both subgroups. a Effect of current and previous choice on IPS2/3 gamma-band activity. b Effect of current and previous choice on IPS0/1 alpha-band activity. c Effect of current and previous action on motor lateralization (pooled signal from M1, PMd/v, IPS/PCeS) in the beta-band. Lower markers indicate timepoints where the fixed effect is significantly different within each group, or between groups ( p < 0.05, FDR-corrected). Data are shown as average fixed effect size +/− 95% confidence intervals.

    Journal: Nature Communications

    Article Title: Persistent activity in human parietal cortex mediates perceptual choice repetition bias

    doi: 10.1038/s41467-022-33237-5

    Figure Lengend Snippet: Time-resolved effect sizes from a general linear mixed model (see “Methods”), separately for both subgroups. a Effect of current and previous choice on IPS2/3 gamma-band activity. b Effect of current and previous choice on IPS0/1 alpha-band activity. c Effect of current and previous action on motor lateralization (pooled signal from M1, PMd/v, IPS/PCeS) in the beta-band. Lower markers indicate timepoints where the fixed effect is significantly different within each group, or between groups ( p < 0.05, FDR-corrected). Data are shown as average fixed effect size +/− 95% confidence intervals.

    Article Snippet: We used general linear mixed effects models (GLMEs, using Matlab’s fitglme ) to quantify the effect of choice history on single-trial power modulation values across all source-level ROIs, frequency ranges and the above-defined time windows.

    Techniques: Activity Assay