transform_data: transform_data

In gladstone-institutes/CalcPower: Power Calculation for biological experiments

transform_data

Description

This functions makes quantile-quanitle (qq) plots of i) raw residual values ii) log-transformed residual values iii) raw residual values after removing outliers, and iv) log-transformed residual values after removing outliers. To detect outliers, the function uses Rosner's test.

Usage

transform_data(
  data,
  condition_column,
  experimental_columns,
  response_column,
  condition_is_categorical,
  repeatable_columns = NA,
  response_is_categorical = FALSE,
  alpha = 0.05
)

Arguments

data	Input data
condition_column	Name of the condition variable (ex variable with values such as control/case). The input file has to have a corresponding column name
experimental_columns	Name of the variable related to experimental design such as "experiment", "plate", and "cell_line".
response_column	Name of the variable observed by performing the experiment. ex) intensity.
condition_is_categorical	Specify whether the condition variable is categorical. TRUE: Categorical, FALSE: Continuous.
repeatable_columns	Name of experimental variables that may appear repeatedly with the same ID. For example, cell_line C1 may appear in multiple experiments, but plate P1 cannot appear in more than one experiment
response_is_categorical	Default: the observed variable is continuous Categorical response variable will be implemented in the future. TRUE: Categorical , FALSE: Continuous (default).
alpha	numeric scalar between 0 and 1 indicating the Type I error associated with the test of outliers

Value

Quantile-quanitle (qq) plots of i) raw residual values ii) log-transformed residual values iii) raw residual values after removing outliers, and iv) log-transformed residual values

Examples

transform_data(data,"classif",c("experiment","line"),"feature1","TRUE")

gladstone-institutes/CalcPower documentation built on Jan. 3, 2023, 11:27 a.m.