sim_2Dimage {BSPBSS} | R Documentation |
The function simulates image data using a probabilistic ICA model whose latent components have specific spatial patterns.
sim_2Dimage(length = 20, n = 50, sigma = 0.002, smooth = 6)
length |
The length of the image. |
n |
sample size. |
sigma |
variance of the noise. |
smooth |
smoothness of the latent components. |
The observations are generated using probabilistic ICA:
X_i(v) = \sum_{j=1}^q A_{i,j} S_j(v) + \epsilon_i(v) ,
where S_j, j=1,...,q
are the latent components, A_{i,j}
is
the mixing coeffecient and \epsilon_i
is the noise term.
Specifically, the number of components in this function is q = 3
,
with each of them being a specific geometric shape. The mixing coefficient matrix
is generated with a von Mises-Fisher distribution with the concentration parameter
being zero, which means it is uniformly distributed on the sphere. \epsilon_i
is a i.i.d. Gaussian noise term with 0 mean and user-specified variance.
List that contains the following terms:
Data matrix with n rows (sample) and p columns (pixel).
Cordinate matrix with p rows (pixel) and d columns (dimension)
Latent components.
Mixing coefficent matrix.
Signal-to-noise ratio.
sim = sim_2Dimage(length = 30, sigma = 5e-4, n = 30, smooth = 6)