Man pages for IPCAPS
Iterative Pruning to Capture Population Structure

cal.eigen.fit(Internal function) Calculae a vector of EigenFit values,...
check.stopping(Internal function) Check whether the IPCAPS process meets...
clustering(Internal function) Perform the clustering process of IPCAPS
clustering.mode(Internal function) Select a clustering method to be used for...
diff.eigen.fit(Internal function) Calculate a vector of different values...
diff.xy(Internal function) Check the different value of X and Y,...
do.glm(Internal function) Perform regression models, internally...
export.groupsExport the IPCAPS result in to a text file
get.node.infoGet the information for specified node
ipcapsPerform unsupervised clustering to capture population...
IPCAPS-packageIPCAPS : Iterative Pruning to CApture Population Structure
labelSynthetic dataset containing population labels for the...
output.template(Internal object) The HTML output template for IPCAPS
pasre.categorical.data(Internal function) Manipulate categorical input files
PCSynthetic dataset containing the top 10 principal components...
postprocess(Internal function) Perform the post-processing step of...
preprocess(Internal function) Perform the pre-processing step of IPCAPS
process.each.node(Internal function) Perform the iterative process for each...
raw.dataSynthetic dataset containing single nucleotide polymorphisms...
replace.missing(Internal function) Replace missing values by specified...
save.eigenplots.htmlGenerate HTML file for EigenFit plots
save.htmlGenerate HTML file for clustering result in text mode
save.plotsWorkflow to generate HTML files for all kinds of plots
save.plots.cluster.htmlGenerate HTML file for scratter plots highlighting data...
save.plots.label.htmlGenerate HTML file for scratter plots highlighting data...
top.discriminatorDetecting top discriminators between two groups
IPCAPS documentation built on May 2, 2019, 11:59 a.m.