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

 popsimA R Documentation

## Simulation time series data for population A

### Description

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

### Usage

``````popsimA
``````

### Format

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

`popsimB`.

### Examples

``````## Generated by the following R codes
set.seed(1234)
n = 50; p = 10; m1 = 20; m2 = 10
Precision1 = Precision2 = diag(rep(1, p))    # generate Precision matrix for population
for (i in 1 : (p - 1)){
temp1 = ifelse(i > 2 * p / 3, -0.2, 0.4)
temp2 = ifelse(i < p / 3, 0.4, -0.2)
Precision1[i, i + 1] = Precision1[i + 1, i] = temp1
Precision2[i, i + 1] = Precision2[i + 1, i] = temp2
}
# R1=-cov2cor(Precision1) + diag(rep(2, p))  # real partial correlation matrix
# R2=-cov2cor(Precision2) + diag(rep(2, p))
Index = matrix(0, p, p)                      # generate covariance matrix for each subject
for (i in 1 : p){
for (j in 1 : p){
if (i != j & abs(i - j) <= 3) Index[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) * Index
RE1 = (RE + t(RE)) / 2
PrecisionInd = Precision1 + RE1
SigmaAll1[, , sub] = solve(PrecisionInd)
}
for (sub in 1 : m2){
RE = matrix(rnorm(p^2, 0, sqrt(2) * 0.15), p, p) * Index
RE1 = (RE + t(RE)) / 2
PrecisionInd = Precision2 + RE1
SigmaAll2[, , sub] = solve(PrecisionInd)
}
rho = 0.3                                    # generate observed time series data
y1 = array(dim = c(n, p, m1))
y2 = array(dim = c(n, p, m2))
for (sub in 1 : m1){
SigmaInd1 = SigmaAll1[, , sub]
ytemp = matrix(0, n, p)
Epsilon = MASS::mvrnorm(n, rep(0, p), SigmaInd1)
ytemp[1, ] = Epsilon[1, ]
for (i in 2 : n){
ytemp[i, ] = rho * ytemp[i - 1, ] + sqrt(1 - rho^2) * Epsilon[i, ]
}
y1[, , sub] = ytemp
}
for (sub in 1 : m2){
SigmaInd2 = SigmaAll2[, , sub]
Xtemp = matrix(0, n, p)
Epsilon = MASS::mvrnorm(n, rep(0, p), SigmaInd2)
ytemp[1, ] = Epsilon[1, ]
for (i in 2 : n){
ytemp[i, ] = rho * ytemp[i - 1, ] + sqrt(1 - rho^2) * Epsilon[i, ]
}
y2[, , sub] = ytemp
}
popsimA = y1
popsimB = y2
``````

BrainCon documentation built on May 31, 2023, 6:36 p.m.