power_envir.calc.linear_outcome {genpwr}

Function to Calculate Power 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

power_envir.calc.linear_outcome(
  N = NULL,
  MAF = NULL,
  ES_G = NULL,
  ES_E = NULL,
  ES_GE = NULL,
  P_e = NULL,
  R2_G = NULL,
  R2_E = NULL,
  R2_GE = NULL,
  sd_y = 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

ES_G

Vector of genetic effect sizes (difference in means) to detect. Either ES_G, ES_E, and ES_EG or R2_G, R2_E, and R2_EG must be specified.

ES_E

Vector of environmental effect sizes (difference in means) to detect. Either ES_G, ES_E, and ES_EG or R2_G, R2_E, and R2_EG must be specified.

ES_GE

Vector of genetic/environment interaction effect sizes (difference in means) to detect. Either ES_G, ES_E, and ES_EG or R2_G, R2_E, and R2_EG must be specified.

P_e

Vector of proportions of the population with exposure to the environmental effect

R2_G

Vector of genetic R-squared values to detect. Either ES_G, ES_E, and ES_EG or R2_G, R2_E, and R2_EG must be specified.

R2_E

Vector of environmental R-squared values to detect. Either ES_G, ES_E, and ES_EG or R2_G, R2_E, and R2_EG must be specified.

R2_GE

Vector of genetic/environment interaction R-squared valuesEither ES_G, ES_E, and ES_EG or R2_G, R2_E, and R2_EG must be specified.

sd_y

Standard deviation of the outcome in the population (ignoring genotype). Either sd_y_x or sd_y 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_envir.calc.linear_outcome(N=100, ES_G = 1.2, ES_E = 1.3, 
	ES_GE = 2, Alpha = 0.05, MAF = 0.2, P_e = 0.2, 
	sd_y = 10, True.Model = "All", Test.Model = "All")