Compute_Power_multi {SPCompute}

Compute the Power of an association study at a given sample size, accommodating more than one covariates, using the Semi-Simulation method.

Description

Compute the Power of an association study at a given sample size, accommodating more than one covariates, using the Semi-Simulation method.

Usage

Compute_Power_multi(
  parameters,
  n,
  response = "binary",
  covariate,
  mode = "additive",
  alpha = 0.05,
  seed = 123,
  searchSizeBeta0 = 8,
  searchSizeGamma0 = 8,
  LargePowerApproxi = FALSE,
  B = 10000
)

Arguments

parameters

A list of parameters that contains all the required parameters in the model. If response is "binary", this list needs to contain "prev" which denotes the prevalence of the disease (or case to control ratio for case-control sampling). If response is continuous, the list needs to contain "traitSD" and "traitMean" which represent the standard deviation and mean of the continuous trait. If covariate is not "none", a parameter "gammaG" needs to be defined to capture the dependence between the SNP and the covariate (through linear regression model if covariate is continuous, and logistic model if covariate is binary). If covariate is "binary", list needs to contains "pE" that defines the frequency of the covariate. If it is continuous, list needs to contain "muE" and "sigmaE" to define its mean and standard deviation. The MAF is defined as "pG", with HWE assumed to hold. Without specifying the parameter "gammaE", by default it is assumed the two covariates are conditionally independent given G. The parameter "gammaE" when specified, should be interpreted as the regression coefficient of the first covariate when regressing the second covariate on it conditional on the SNP G.

n

An integer number that indicates the sample size.

response

A string of either "binary" or "continuous", indicating the type of response/trait variable in the model, by default is "binary"

covariate

A vector of length two with elements being either c("binary", "continuous"),c("binary", "binary") or c("continuous", "continuous"), indicating the type of covariate E in the model.

mode

A string of either "additive", "dominant" or "recessive", indicating the genetic mode, by default is "additive".

alpha

A numeric value that denotes the significance level used in the study, by default is 0.05.

seed

An integer number that indicates the seed used for the simulation to compute the approximate fisher information matrix, by default is 123.

searchSizeBeta0

The interval radius for the numerical search of beta0, by default is 8. Setting to higher values may solve some numerical problems at the cost of longer runtime.

searchSizeGamma0

The interval radius for the numerical search of gamma0, by default is 8. Setting to higher values may solve some numerical problems at the cost of longer runtime.

LargePowerApproxi

TRUE or FALSE indicates whether to use the large power approximation formula.

B

An integer number that indicates the number of simulated sample to approximate the fisher information matrix, by default is 10000 (Should only be changed if computation uses semi-simulation method).

Value

The power that can be achieved at the given sample size.

Examples

parameters <- list(TraitMean = 0.3, TraitSD = 1, pG = 0.2, betaG = log(1.1),
betaE = c(log(1.1), log(1.2)),
muE = 0, sigmaE = 3, gammaG = c(log(2.1), log(2.2)), pE = 0.4)
SPCompute::Compute_Power_multi(parameters, n = 1000, response = "continuous",
covariate = c("binary", "continuous"))