sump: Identifying outlier using sum p based meta-analysis method

In BudhlakotiN/OGS: Outlier in Genomics Data

Description

Identifies outlier in genomics data by combining various LASSO diagnostic followed by meta- analysis using sum of p, also known as Edgington’s method.

Usage

  OGS.sump(X,Y)

Arguments

X	X is a design matrix of marker genotype of size n×p where n are no of Individuals under study (i.e. genotype, lines) and p are no of markers
Y	Y is a vector of response/individuals of size n×1

Details

This function first combines various LAASO diagnostic proposed for high dimensional regression i.e. df-model, df-lambda, df-regpath and df-cvpath and produce a single p-value for each individuals/observation using sum p method. Using a suitable p-value cut-off (i.e. .01 or .05), outlier can be identified.

Value

$p-values lists p-value for each observation/individuals/lines

References

Rajaratnam, B., Roberts, S., Sparks, D. & Yu, H. Influence Diagnostics for High-Dimensional Lasso Regression. Journal of Computational and Graphical Statistics, 1-14 (2019).

Edgington ES (1972). “An additive method for combining probability values from independent experiments.” Journal of Psychology, 80, 351–363.

Examples

  library(STGS)
  data(wheat_data)
  X<-wheat_data[,1:100]
  Y<-as.data.frame(wheat_data[,101])
  OGS.sump(X,Y)

BudhlakotiN/OGS documentation built on Jan. 6, 2020, 12:43 a.m.