genpwr (version 1.0.1)

ss_linear_envir.calc.logistic_outcome: Function to Calculate Sample Size for Linear Models with logistic environment interaction

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

ss_linear_envir.calc.logistic_outcome(power = 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

power

Vector of the desired power(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

# NOT RUN {
ss <- ss_linear_envir.calc.logistic_outcome(power=0.8, 
	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")


# }