preprocess: (Internal function) Perform the pre-processing step of IPCAPS
In IPCAPS: Iterative Pruning to Capture Population Structure

(Internal function) Perform the pre-processing step of IPCAPS

preprocess(
  files,
  label.file,
  lab.col,
  rdata.infile,
  bed.infile,
  cate.list,
  result.dir,
  threshold,
  min.fst,
  max.thread = NA,
  reanalysis = FALSE,
  method = "mix",
  min.in.group = 20,
  datatype = "snp",
  nonlinear = FALSE,
  missing.char = NA,
  regression.file = NA,
  regression.col.first = NA,
  regression.col.last = NA,
  reg.method = "linear",
  plot.as.pdf = NA,
  no.plot = NA
)

`files`	IPCAPS supports SNPs encoded as 0, 1 and 2 (dosage encoding). Rows represent SNPs and columns represent subjects. Each column needs to be separated by a space or a tab. A big text file should be divided into smaller files to load faster. For instance, to input 3 files, use as: files=c( 'input1.txt', 'input2.txt', 'input3.txt').
`label.file`	An additional useful information (called "labels" in IPCAPS) related subject, for example, geographic location or disease phenotype. These labels (one at a time) are used in displaying the clustering outcome of IPCAPS. A label file must contain at least one column. However, it may contain more than one column in which case each column need to be separated by a space or a tab.
`lab.col`	The label in the label file to be used in the tree-like display of IPCAPS clustering results.
`rdata.infile`	In case of re-analysis, it is convenient to run IPCAPS using the file rawdata.RData generated by IPCAPS. This file contains a matrix of SNPs (raw.data) and a vector of labels (label).
`bed.infile`	A PLINK binary format consists of 3 files; bed, bim, and fam. To generate these files from PLINK, use option –make-bed. See more details at: http://zzz.bwh.harvard.edu/plink/data.shtml.
`cate.list`	(Unimplemented) A list of categorical input file (text). For instance, to input 3 files, use as: files=c('input1.txt', 'input2.txt', 'input3.txt').
`result.dir`	To set an absolute path for IPCAPS output. If the specified output directory already exists, result files are saved in sub-directories cluster_out, cluster_out1, cluster_out2, etc.
`threshold`	Cutoff value for EigenFit.
`min.fst`	Minimum Fst between a pair of subgroups.
`max.thread`	(Require the parallelization patch) Maximum number of concurrent threads.
`reanalysis`	(Unimplemented) To specify whether it is re-analysis or not. If TRUE, it is re-analysis, otherwise it is not. Default = FALSE.
`method`	The internal clustering method. It can be set to "mix" (rubikclust & mixmod), "mixmod" (Lebret et al., 2015), "clara" (R: Clustering Large Applications), "pam" (R: Partitioning Around Medoids (PAM) Object), "meanshift" (Wang, 2016), "apcluster" (Bodenhofer et al., 2016), and "hclust" (R: Hierarchical Clustering). Default = "mix".
`min.in.group`	Minimum number of individuals to constitute a cluster or subgroup.
`datatype`	To specify whether the input data are 'snp' or other type. Defalut = 'snp'.
`nonlinear`	(Unimplemented) To specify whether linear or non-linear method is used for IPCAPS analysis. If TRUE, non-linear method is used, otherwise linear method is used. Default = FALSE.
`missing.char`	Symbol used for missing genotypes. Default = NA.
`regression.file`	A file of covariates; one covariate per column. SNPs can be adjusted for these covariates via regression modeling and residual computation.
`regression.col.first`	Refer to a covariate file, the first covariate to be considered as confounding variable.
`regression.col.last`	Refer to a covariate file, the last covariate to be considered as confounding variable. All the variables in between the cov.col.first and cov.col.last will be considered in the adjustment process.
`reg.method`	(Fixed) Specify a method for regression analysis. Default = 'linear'.
`plot.as.pdf`	To export plots as PDF. When omitted, plots are saved as PNG.
`no.plot`	No plot is generated if this option is TRUE. This option is useful when the system does not support X Windows in the unix based system. Default = FALSE.