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ipcaps_example
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Synthetic dataset containing the top 10 principal components (PC) from the
dataset raw.data |
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cal.eigen.fit()
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(Internal function) Calculae a vector of EigenFit values, internally used for
parallelization |
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check.stopping()
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(Internal function) Check whether the IPCAPS process meets the stopping
criterion. |
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clustering()
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(Internal function) Perform the clustering process of IPCAPS |
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clustering.mode()
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(Internal function) Select a clustering method to be used for the IPCAPS
process. |
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diff(<eigen.fit>)
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(Internal function) Calculate a vector of different values from a vector of
EigenFit values, internally used for parallelization |
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diff(<xy>)
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(Internal function) Check the different value of X and Y, internally used for
parallelization |
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do.glm()
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(Internal function) Perform regression models, internally used for
parallelization |
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export.groups()
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Export the IPCAPS result to a text file |
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get.node.info()
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Get the information for specified node |
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ipcaps()
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Perform unsupervised clustering to capture population structure based on iterative pruning |
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ipcaps_example
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Synthetic dataset containing population labels for the dataset raw.data |
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output.template
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(Internal object) The HTML output template for IPCAPS |
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pasre.categorical.data()
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(Internal function) Manipulate categorical input files |
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postprocess()
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(Internal function) Perform the post-processing step of IPCAPS |
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preprocess()
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(Internal function) Perform the pre-processing step of IPCAPS |
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process.each.node()
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(Internal function) Perform the iterative process for each node |
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ipcaps_example
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Synthetic dataset containing single nucleotide polymorphisms (SNP) |
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replace.missing()
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(Internal function) Replace missing values by specified values, internally
used for parallelization |
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save.eigenplots.html()
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Generate HTML file for EigenFit plots |
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save.html()
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Generate HTML file for clustering result in text mode |
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save.plots()
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Workflow to generate HTML files for all kinds of plots |
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save.plots.cluster.html()
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Generate HTML file for scatter plots which all data points are highlighted by IPCAPS clusters |
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save.plots.label.html()
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Generate HTML file for scatter plots which data points are highlighted by given labels |
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top.discriminator()
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Detecting top discriminators between two groups |