power_linear_envir.calc.logistic_outcome: Function to Calculate Power for Linear Models with logistic...

In genpwr: Power Calculations Under Genetic Model Misspecification

Description

Calculates the power to detect an difference in means/effect size/regression coefficient, at a given sample size, N, with type 1 error rate, Alpha

Usage

power_linear_envir.calc.logistic_outcome(
  N = NULL,
  MAF = NULL,
  OR_G = NULL,
  OR_E = NULL,
  OR_GE = NULL,
  sd_e = NULL,
  Case.Rate = NULL,
  k = NULL,
  Alpha = 0.05,
  True.Model = "All",
  Test.Model = "All"
)

Arguments

N	Vector of the desired sample size(s)
MAF	Vector of minor allele frequencies
OR_G	Vector of genetic odds ratios to detect
OR_E	Vector of environmental odds ratios to detect
OR_GE	Vector of genetic/environmental interaction odds ratios to detect
sd_e	Standard deviation of the environmental variable
Case.Rate	Standard deviation of the outcome in the population (ignoring genotype). Either Case.Rate_x or Case.Rate must be specified.
k	Vector of the number of controls per case. Either k or Case.Rate must be specified.
Alpha	the desired type 1 error rate(s)
True.Model	A vector specifying the true underlying genetic model(s): 'Dominant', 'Additive', 'Recessive' or 'All'
Test.Model	A vector specifying the assumed genetic model(s) used in testing: 'Dominant', 'Additive', 'Recessive' or 'All'

Value

A data frame including the power for all combinations of the specified parameters (Case.Rate, ES, Power, etc)

Examples

pw <- power_linear_envir.calc.logistic_outcome(N=30, 
	OR_G=1.1, OR_E=1.2, OR_GE=1.5, 
	sd_e = 1, MAF=0.2, Case.Rate = 0.2,
	Alpha=0.05, True.Model="All", Test.Model="All")

genpwr documentation built on March 31, 2021, 1:06 a.m.