ConfoundingExplorer: Confounding explorer

View source: R/ConfoundingExplorer.R

ConfoundingExplorerR Documentation

Confounding explorer

Description

Confounding explorer

Usage

ConfoundingExplorer(
  sampleSizes = matrix(rep(5, 4), nrow = 2, dimnames = list(c("group1", "group2"),
    c("batch1", "batch2"))),
  fracVarCond = 0.25,
  fracVarBatch = 0.5,
  fracVarUnknown = 0,
  condEffectSize = 3,
  batchEffectSize = 3,
  unknownEffectSize = 0,
  unknownEffectType = "categorical",
  analysisApproach = "dontAdjust",
  seed = 123
)

Arguments

sampleSizes

2x2 numeric matrix giving the number of samples in each group. Row names must be c('group1', 'group2') and column names must be c('batch1', 'batch2').

fracVarCond, fracVarBatch, fracVarUnknown

Numeric scalars between 0 and 1. The fraction of variables affected by the condition effect, batch effect, and 'unknown' effect, respectively.

condEffectSize, batchEffectSize, unknownEffectSize

Numeric scalars. The condition, batch and 'unknown' effect size, respectively.

unknownEffectType

Character scalar, either 'categorical' or 'continuous', representing the type of 'unknown' effect to add.

analysisApproach

Character scalar. One of 'dontAdjust', 'inclBatch', 'removeBatch', 'removeBatchAccCond'. Determines what model is fit to the data.

seed

Numeric scalar, the random seed to use when simulating data.

Value

A shinyApp object

Author(s)

Charlotte Soneson

Examples

if (interactive()) {
  ConfoundingExplorer()
}


csoneson/ConfoundingExplorer documentation built on May 21, 2022, 6:46 p.m.