# popsimA: Simulation time series data for population A In BrainCon: Inference the Partial Correlations Based on Time Series Data

## Description

A dataset containing values of 10 interested variables of 20 subjects over 50 periods.

## Usage

 `1` ```popsimA ```

## Format

An object of class `array` of dimension 50 x 10 x 20.

`popsimB`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66``` ```## Generated by the following R codes set.seed(1000) n = 50 p = 10 m1 = 20; m2 = 15 precision1 = diag(rep(1, p)) # generate precision matrix for (i in 1 : (p - 1)){ if (i <= 2 * p / 3) temp = -0.05 if (i > 2 * p / 3) temp = -0.2 precision1[i, i + 1] = temp precision1[i + 1, i] = temp } res = matrix(0, p, p) for (i in 1 : p){ for (j in 1 : p){ if (abs(i - j) == 1) res[i, j] = 0.15 } } precision2 = precision1 + res Index0 = matrix(0, p, p) # generate covariance matrix for (i in 1 : p){ for (j in 1 : p){ if (i != j & abs(i - j) <= 5) Index0[i, j] = 1 } } SigmaAll1 = array(dim = c(p, p, m1)) SigmaAll2 = array(dim = c(p, p, m2)) for (sub in 1 : m1){ RE = matrix(rnorm(p^2, 0, sqrt(2) * 0.05), p, p) * Index0 RE1 = (RE + t(RE)) / 2 precisionMatrixInd1 = precision1 + RE1 SigmaInd1 = solve(precisionMatrixInd1) SigmaAll1[, , sub] = SigmaInd1 } for (sub in 1 : m2){ RE = matrix(rnorm(p^2, 0, sqrt(2) * 0.05), p, p) * Index0 RE1 = (RE + t(RE)) / 2 precisionMatrixInd2 = precision2 + RE1 SigmaInd2 = solve(precisionMatrixInd2) SigmaAll2[, , sub] = SigmaInd2 } rho = 0.5 Z1 = array(dim = c(n, p, m1)) # observed time series data Z2 = array(dim = c(n, p, m2)) for (sub in 1 : m1){ SigmaInd1 = SigmaAll1[, , sub] Xtemp = matrix(0, n, p) Epsilon = MASS::mvrnorm(n, rep(0, p), SigmaInd1) Xtemp[1, ] = Epsilon[1, ] for (i in 2 : n){ Xtemp[i, ] = rho * Xtemp[i - 1, ] + sqrt(1 - rho^2) * Epsilon[i, ] } Z1[, , sub] = Xtemp } for (sub in 1 : m2){ SigmaInd2 = SigmaAll2[, , sub] Xtemp = matrix(0, n, p) Epsilon = MASS::mvrnorm(n, rep(0, p), SigmaInd2) Xtemp[1, ] = Epsilon[1, ] for (i in 2 : n){ Xtemp[i, ] = rho * Xtemp[i - 1, ] + sqrt(1 - rho^2) * Epsilon[i, ] } Z2[, , sub] = Xtemp } popsimA = Z1 popsimB = Z2 ```