TensorPreAve (version 1.1.0)

pre_eigenplot: Eigenvalue Plot of a Random Sample

Description

Function to plot the eigenvalues of the sample covariance matrix of a randomly chosen sample.

Usage

pre_eigenplot(X, k)

Arguments

X

A 'Tensor' object defined in package rTensor with \(K+1\) modes. Mode-1 should correspond to the time mode.

k

The mode to plot the eigenvalues for.

Details

Input a tensor time series and a mode index, output the plot of eigenvalues of the sample covariance matrix of the given mode, with a randomly chosen sample of the mode-\(k\) fibres. This helps users to choose the parameter eigen_j in function pre_est. A large dip should be observed at the (\(r_k+1\))-th position of the plot, and user can choose eigen_j to be a bit larger than the position of dip observed to avoid missing potential weak factors. If such a dip is not observed, try to run the function for a few times until it can be observed.

Examples

# Example of a real data set
set.seed(800)
pre_eigenplot(value_weight_tensor, k = 2)

# \donttest{
# Example using generated data
K = 2
T = 100
d = c(40,40)
r = c(2,2)
re = c(2,2)
eta = list(c(0,0),c(0,0))
u = list(c(-2,2),c(-2,2))
set.seed(10)
Data_test = tensor_data_gen(K,T,d,r,re,eta,u)
X = Data_test$X
pre_eigenplot(X, k = 1)
# }