7 datasets found
  1. R code

    • figshare.com
    txt
    Updated Jun 5, 2017
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    Christine Dodge (2017). R code [Dataset]. http://doi.org/10.6084/m9.figshare.5021297.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Jun 5, 2017
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Christine Dodge
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    R code used for each data set to perform negative binomial regression, calculate overdispersion statistic, generate summary statistics, remove outliers

  2. f

    Data from: Error and anomaly detection for intra-participant time-series...

    • tandf.figshare.com
    xlsx
    Updated Jun 1, 2023
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    David R. Mullineaux; Gareth Irwin (2023). Error and anomaly detection for intra-participant time-series data [Dataset]. http://doi.org/10.6084/m9.figshare.5189002
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    David R. Mullineaux; Gareth Irwin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.

  3. f

    Additional file 2 of Thresher: determining the number of clusters while...

    • springernature.figshare.com
    zip
    Updated Jun 3, 2023
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    Min Wang; Zachary B. Abrams; Steven M. Kornblau; Kevin R. Coombes (2023). Additional file 2 of Thresher: determining the number of clusters while removing outliers [Dataset]. http://doi.org/10.6084/m9.figshare.5768622.v1
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    zipAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    figshare
    Authors
    Min Wang; Zachary B. Abrams; Steven M. Kornblau; Kevin R. Coombes
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    R Code for Analyses. This is a zip file containing all of the R code used to perform simulations and to analyze the breast cancer data. (ZIP 407 kb)

  4. Pearson correlations (r) between siblings for Eyes scores and Eyes scores...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Gillian Ragsdale; Robert A. Foley (2023). Pearson correlations (r) between siblings for Eyes scores and Eyes scores adjusted by removing the low-scoring outliers (Eyes Adj >17). [Dataset]. http://doi.org/10.1371/journal.pone.0023236.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Gillian Ragsdale; Robert A. Foley
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    **Correlation is significant at the 0.01 level (2-tailed).*Correlation is significant at the 0.05 level (2-tailed).'Correlation is significant at the 0.1 level (2-tailed).For each model, the two categories of sibling pairs are derived from Table 2. In each case, a possible fit (in bold) is indicated by the second correlation being less than the first.

  5. Causal effect estimates using Radial MVMR with and without outlier removal...

    • plos.figshare.com
    xls
    Updated Dec 30, 2024
    + more versions
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    Wes Spiller; Jack Bowden; Eleanor Sanderson (2024). Causal effect estimates using Radial MVMR with and without outlier removal with varying levels of unbalanced pleiotropy. [Dataset]. http://doi.org/10.1371/journal.pgen.1011506.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Dec 30, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Wes Spiller; Jack Bowden; Eleanor Sanderson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Causal effect estimates using Radial MVMR with and without outlier removal with varying levels of unbalanced pleiotropy.

  6. MLR models of age at onset of T1D after removing outliers (N = 354).

    • plos.figshare.com
    xls
    Updated Jun 16, 2023
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    Ahood Alazwari; Mali Abdollahian; Laleh Tafakori; Alice Johnstone; Rahma A. Alshumrani; Manal T. Alhelal; Abdulhameed Y. Alsaheel; Eman S. Almoosa; Aseel R. Alkhaldi (2023). MLR models of age at onset of T1D after removing outliers (N = 354). [Dataset]. http://doi.org/10.1371/journal.pone.0264118.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Ahood Alazwari; Mali Abdollahian; Laleh Tafakori; Alice Johnstone; Rahma A. Alshumrani; Manal T. Alhelal; Abdulhameed Y. Alsaheel; Eman S. Almoosa; Aseel R. Alkhaldi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    MLR models of age at onset of T1D after removing outliers (N = 354).

  7. f

    RRegrs study for Growth Yield

    • figshare.com
    txt
    Updated Jun 5, 2016
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    Cristian Robert Munteanu (2016). RRegrs study for Growth Yield [Dataset]. http://doi.org/10.6084/m9.figshare.3409804.v2
    Explore at:
    txtAvailable download formats
    Dataset updated
    Jun 5, 2016
    Dataset provided by
    figshare
    Authors
    Cristian Robert Munteanu
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    RRegrs study for Growth Yield for original and corrected/filterred datasets: inputs training and test files, R scripts to split the datasets, plot for outlier removal.

  8. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Christine Dodge (2017). R code [Dataset]. http://doi.org/10.6084/m9.figshare.5021297.v1
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R code

Explore at:
txtAvailable download formats
Dataset updated
Jun 5, 2017
Dataset provided by
Figsharehttp://figshare.com/
Authors
Christine Dodge
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

R code used for each data set to perform negative binomial regression, calculate overdispersion statistic, generate summary statistics, remove outliers

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