identify_outliars: Identify outliars
In asalzy/mrtryxhelpR: Helper functions for the tryx package

View source: R/test.R

identify_outliars

R Documentation

Identify outliars

Description

This function takes the output from TwoSampleMR::harmonise_data() function and a list object including identified outliars

Usage

identify_outliars(
  dat,
  outliers = "RadialMR",
  outlier_correction = "none",
  outlier_threshold = ifelse(outlier_correction == "none", 0.05/nrow(dat), 0.05),
  use_proxies = FALSE,
  search_correction = "none",
  search_threshold = ifelse(search_correction == "none", 5e-08, 0.05),
  id_list = "default",
  include_outliers = FALSE,
  mr_method = "mr_ivw"
)

Arguments

`dat`	Output from harmonise_data. Note - only the first id.exposure - id.outcome pair will be used.
`outliers`	Default is to use the RadialMR package to identify IVW outliers. Alternatively can providen an array of SNP names that are present in dat$SNP to use as outliers.
`outlier_correction`	Defualt = "none", but can select from ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none").
`outlier_threshold`	If outlier_correction = "none" then the p-value threshold for detecting outliers is by default 0.05.
`use_proxies`	Whether to use proxies when looking up associations. FALSE by default for speed.
`search_correction`	Default = "none", but can select from ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none").
`search_threshold`	If search_correction = "none" then the p-value threshold for detecting an association between an outlier and a candidate trait is by default 5e-8. Otherwise it is 0.05.
`id_list`	The list of trait IDs to search through for candidate associations. The default is the high priority traits in available_outcomes().
`include_outliers`	When performing MR of candidate traits against exposures or outcomes, whether to include the original outlier SNP. Default is FALSE.
`mr_method`	Method to use for candidate trait - exposure/outcome analysis. Default is mr_ivw. Can also provide basic MR methods e.g. mr_weighted_mode, mr_weighted_median etc. Also possible to use "strategy1" which performs IVW in the first instance, but then weighted mode for associations with high heterogeneity.