Description Usage Arguments Value Examples

This function does the preliminary data management and fit the model under null hypothesis. The output will be used in the other WGScan functions.

1 | ```
WGScan.prelim(Y, X=NULL, id=NULL, out_type="C", B=5000)
``` |

`Y` |
The outcome variable, an n*1 matrix where n is the total number of observations |

`X` |
An n*d covariates matrix where d is the total number of covariates. |

`id` |
The subject id. This is used to match phenotype with genotype. The default is NULL, where the matched phenotype and genotype matrices are assumed. |

`out_type` |
Type of outcome variable. Can be either "C" for continuous or "D" for dichotomous. The default is "C". |

`B` |
Number of resampling replicates. The default is 5000. A larger value leads to more accurate and stable p-value calculation, but requires more computing time. |

It returns a list used for function WGScan.Region(), WGScan.SingleWindow() and WGScan.VCF.chr().

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
library(WGScan)
# Load data example
# Y: outcomes, n by 1 matrix where n is the total number of observations
# X: covariates, n by d matrix
# G: genotype matrix, n by p matrix where n is the total number of subjects
# Z: functional annotation matrix, p by q matrix
data(WGScan.example)
Y<-WGScan.example$Y;X<-WGScan.example$X;G<-WGScan.example$G;Z<-WGScan.example$Z
# Preliminary data management
result.prelim<-WGScan.prelim(Y,X=X,out_type="C",B=5000)
``` |

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