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matlab toolbox spm  (MathWorks Inc)


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    MathWorks Inc matlab toolbox spm
    Matlab Toolbox Spm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2255 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/matlab toolbox spm/product/MathWorks Inc
    Average 96 stars, based on 2255 article reviews
    matlab toolbox spm - by Bioz Stars, 2026-03
    96/100 stars

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    Fig. 1. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different shoes (H: 50 mm, M: 35 mm and L: 27 mm) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of two running speed conditions are represented for each shoe condition. Lower row shows the corresponding SPM rmANOVA results for the main factor shoe. F* values indicate the corresponding thresholds for α = 0.05.

    Journal: Journal of biomechanics

    Article Title: The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

    doi: 10.1016/j.jbiomech.2025.112615

    Figure Lengend Snippet: Fig. 1. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different shoes (H: 50 mm, M: 35 mm and L: 27 mm) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of two running speed conditions are represented for each shoe condition. Lower row shows the corresponding SPM rmANOVA results for the main factor shoe. F* values indicate the corresponding thresholds for α = 0.05.

    Article Snippet: The time series of the three UCM components (UCM‖, UCM�, UCMratio) were analyzed with statistical parametric mapping (SPM) repeated measures analysis of variance (rmANOVA) by using the SPM toolbox in MATLAB (spm1d toolbox; (Pataky et al., 2019).

    Techniques:

    Fig. 2. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different running speeds (10 km/h and 15 km/h) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of three shoe conditions are represented for each running speed. The significant speed effects in SPM rmANOVA are shown as the gray areas. Lower row shows the corresponding SPM rmANOVA results for the main factor speed with clustered p- values. F* values indicate the corresponding thresholds for α = 0.05.

    Journal: Journal of biomechanics

    Article Title: The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

    doi: 10.1016/j.jbiomech.2025.112615

    Figure Lengend Snippet: Fig. 2. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different running speeds (10 km/h and 15 km/h) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of three shoe conditions are represented for each running speed. The significant speed effects in SPM rmANOVA are shown as the gray areas. Lower row shows the corresponding SPM rmANOVA results for the main factor speed with clustered p- values. F* values indicate the corresponding thresholds for α = 0.05.

    Article Snippet: The time series of the three UCM components (UCM‖, UCM�, UCMratio) were analyzed with statistical parametric mapping (SPM) repeated measures analysis of variance (rmANOVA) by using the SPM toolbox in MATLAB (spm1d toolbox; (Pataky et al., 2019).

    Techniques:

    Fig. 3. SPM rmANOVA results for the interaction effects between shoe and speed conditions. F* values indicate the corresponding thresholds for α = 0.05.

    Journal: Journal of biomechanics

    Article Title: The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

    doi: 10.1016/j.jbiomech.2025.112615

    Figure Lengend Snippet: Fig. 3. SPM rmANOVA results for the interaction effects between shoe and speed conditions. F* values indicate the corresponding thresholds for α = 0.05.

    Article Snippet: The time series of the three UCM components (UCM‖, UCM�, UCMratio) were analyzed with statistical parametric mapping (SPM) repeated measures analysis of variance (rmANOVA) by using the SPM toolbox in MATLAB (spm1d toolbox; (Pataky et al., 2019).

    Techniques: