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glmval function  (MathWorks Inc)


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    MathWorks Inc glmval function
    Glmval Function, 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/glmval+function/pmc11192533-204-7-10
    Average 90 stars, based on 1 article reviews
    glmval function - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Links between temporal acuity and multisensory integration across lifespan
    Article Snippet: The offset at which individuals obtained a 75% accuracy rate through the MatLab glmval function.

    Article Title: A novel method for estimating properties of attentional oscillators reveals an age-related decline in flexibility
    Article Snippet: We then evaluated these models, using the glmval function in MATLAB to obtain predicted accuracy values for each participant and session.

    Article Title: Resting-state connectivity within the brain’s reward system predicts weight loss and correlates with leptin
    Article Snippet: The average Z -values for each ROI were then, respectively, used to conduct 44 linear regressions that determined independent weights of the vmPFC-to-voxel connectivity on weight loss (kilogram at T8 minus kilogram at T0) for more than 43 participants (leaving out the 44th participant) following : (1) T 8 k g - T 0 k g = β 0 + β ROI × ZvmPFC - ROI + ε Each time, the weight ( β ROI ) of the vmPFC-ROI connectivity on weight loss obtained from 43 participants together with the Z -value for vmPFC-ROI connectivity extracted from the ROI in the left-out participant was regressed to predict weight loss for the left-out participant y ( ^ l eft out participant ) using the glmval function in matlab as in equation (2): (2) y ^ ( left out participant ) = β ROI 43 × ZvmPFC - ROI ( left out participant ) + ε Last, we quantified the association between the predicted and the observed levels of weight loss by using Pearson’s correlation, which was tested for significance by using both parametric one-sampled t -tests and non-parametric permutation tests (10 000 permutations).

    Article Title: Switch-Related and General Preparation Processes in Task-Switching: Evidence from Multivariate Pattern Classification of EEG Data
    Article Snippet: Here, the model (via Matlab's glmval function) used these weights to classify repeat and switch-away trials as being more similar to switch-to or non-informative trials.

    Article Title: Depression-related anterior cingulate prefrontal resting state connectivity normalizes following cognitive behavioral therapy
    Article Snippet: The predicted value of the left out sample was then estimated using the fitted model from the training set (using glmval function in Matlab).

    Article Title: Switch-Related and General Preparation Processes in Task-Switching: Evidence from Multivariate Pattern Classification of EEG Data
    Article Snippet: Here, the model (via Matlab’s glmval function) used these weights to classify repeat and switch-away trials as being more similar to switch-to or noninformative trials.

    Article Title: A microfluidic assay for the quantification of the metastatic propensity of breast-cancer specimens
    Article Snippet: Probability values were calculated in MATLAB using the glmval function.

    Generated:

    Article Title: Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees
    Article Snippet: The 95% confidence intervals for performance estimate for overall and single textures decoding performances were computed for every code with exact Clopper Pearson method (binofit function in Matlab) and compared against chance level (3/8 for gratings, 1/4 or 1/6 for naturalistic textures) to assess performance significance. .. Parameters for logistic fit of performance as a function of spatial periods for gratings coarseness discrimination were estimated with generalized linear regression (glmfit function in Matlab), then the optimal logistic fit function was generated (glmval function in Matlab) and its accuracy evaluated as the squared correlation coefficient between data and fit. ..



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    Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link using MATLAB’s glmfit and glmval functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

    Journal: Biomaterials

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    doi: 10.1016/j.biomaterials.2022.121540

    Figure Lengend Snippet: Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link using MATLAB’s glmfit and glmval functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

    Article Snippet: The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link using MATLAB’s glmfit and glmval functions.

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