# R/simulateData.R In angeella/ARIpermutation: Permutation-Based All-Resolutions Inference Method

#### Documented in simulateData

#' @title simulate normal distributed data
#' @description Simulate normal distributed data.
#' @usage simulateData(pi0,m,n, rho, seed = NULL, power = 0.8, alpha = 0.05)
#' @param pi0 numeric value in [0,1]. Proportion of true null hypothesis.
#' @param m numeric value. Number of variables.
#' @param n numeric value. Number of observations.
#' @param rho numeric value in [0,1]. Level of equi-correlation between pairs of variables.
#' @param seed integer value. If you want to specify the seed. Default to @NULL
#' @param power numeric value in [0,1]. Level of power. Default 0.8.
#' @param alpha numeric value in [0,1]. It expresses the alpha level to control the family-wise error rate. Default 0.05.
#' @author Angela Andreella
#' @return Returns a matrix with dimensions \eqn{m \times n}.
#' @export
#' @importFrom stats rnorm
#' @importFrom stats power.t.test

simulateData <- function(pi0,m,n, rho, seed = NULL, power = 0.8, alpha = 0.05){
if(is.null(n) & !is.null(power)){stop("Please insert sample size n")}
m0 = round(m*pi0)
m1 = round(m -m0)
if(is.null(seed)){set.seed(sample.int(1e5, 1))}else{set.seed(seed)}
pwo <- power.t.test(power = power, n=n, sig.level = alpha, type = "one.sample", alternative = "two.sided", sd = 1)
diff_mean<-pwo$delta #diff_mean <-rgamma(m1, shape = pwo$delta, scale = 1)
#diff_mean <- rnorm(m1, mean = pwo\$delta, sd = 1000)
if(is.null(seed)){set.seed(sample.int(1e5, 1))}else{set.seed(seed)}
eps <- sqrt(1-rho)*matrix(rnorm(m*n), ncol=m)  + sqrt(rho)*matrix(rep(rnorm(n),m), ncol=m)
#mu <- c(diff_mean, rep(0, m0))
mu <- c(rep(diff_mean,m1), rep(0, m0))
X <- matrix(rep(mu, each=n), ncol=m) + eps

return(t(X))
}

angeella/ARIpermutation documentation built on Jan. 12, 2022, 1:01 p.m.