# demo/dpca.simulation.R In freqdom: Frequency Domain Based Analysis: Dynamic PCA

```library("freqdom")

set.seed(1)
n = 100

# We illustrate here an extreme case of very strong
# temporal dependance in the process
# Generate n observations of a AR(1) process
P = matrix(c(1,-9,-3,1),2,2)
P = 0.9* P / norm(P,type="O")
X = rar(n, Psi=P, sigma = diag(2)*0.1)

# standard PCA
PR = prcomp(X)
PR\$x = X %*% t(PR\$rotation)
PR\$x[,2] = 0 # use only the first component
X.est.static = PR\$x %*% PR\$rotation

# dynamic PCA
XI = dpca.filters(spectral.density(X,q=15),q=10)
Y = X %c% XI
Y[,2] = 0 # use only the first score
X.est = Y  %c% freqdom.transpose(rev(XI)) # recover X from first score

# Compute variance explained by PCA
M1 = MSE(X,X.est.static)
M = MSE(X,0)

cat("NMSE PCA =  ")
cat(M1/M) # approximation error of the first score

# Compute variance explained by DPCA
M1 = MSE(X,X.est)
cat("\nNMSE DPCA = ")
cat(M1/M) # approximation error of the first score
cat("\n")

# Plot scores
par(mfrow=c(1,3))

ind = 1:20

matplot(X[ind,],t='l',xlab="time")
title("Original time series")
matplot(X.est.static[ind,],xlab="time",t='l')
title("PCA recovered time series")
matplot(X.est[ind,],xlab="time",t='l')
title("DPCA recovered time series")
par(mfrow=c(1,1))
```

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freqdom documentation built on Oct. 4, 2022, 5:05 p.m.