Description Usage Arguments Value Author(s) References Examples
Computes the minimum number of eigenvalues required so that
the given proportion of the variance of x
over each segment,
defined by the change-points detected from the common components,
by varying the proportion and plots the number of eigenvalues.
1 | post.cpts.analysis(x, est.cps, cutoff.seq = seq(0.5, 0.95, by = 0.05), do.plot = TRUE)
|
x |
input time series |
est.cps |
a vector containing the change-points estimated for the common components |
cutoff.seq |
a sequence containing the proportions between zero and one |
do.plot |
if |
a matrix containing the minimum numbers of eigenvalues for varying levels of proportions over the segments defined by two neighbouring change-points in the common components
Haeran Cho
M. Barigozzi, H. Cho and P. Fryzlewicz (2016) Simultaneous multiple change-point and factor analysis for high-dimensional time series, Preprint.
1 2 3 4 5 6 7 8 9 10 11 12 13 | n <- 50; T <- 200
e <- matrix(rnorm(n*T), nrow=n) # idiosyncratic components
r <- 3 # factor number
Lam <- matrix(rnorm(n*r, 1, 1), nrow=n) # loadings
f <- matrix(rnorm(r*T), nrow=r) # factors
chi <- e*0 # common component
chp <- T/2 # change-point
chi[, 1:chp] <- Lam%*%f[, 1:chp]
Lam <- Lam + matrix(rnorm(n*r, 0, sqrt(2)), nrow=n) # new loadings
chi[, (chp+1):T] <- Lam%*%f[, (chp+1):T]
x <- chi + sqrt(r)*e
post.cpts.analysis(x, 100)
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