gppca-class: GPPCA class

In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

GPPCA class

Description

An S4 class for generalized probabilistic principal component analysis of correlated data.

Objects from the Class

Objects of this class are created and initialized using the gppca function to set up the estimation.

Slots

input:

object of class vector, the length is equivalent to the number of observations.

output:

object of class matrix. The observation matrix.

d:

object of class integer to specify the number of latent factors.

est_d:

object of class logical, default is FALSE. If TRUE, d will be estimated by either variance matching (when noise level is given) or information criteria (when noise level is unknown). Otherwise, d is fixed, and users must assign a value to d.

shared_params:

object of class logical, default is TRUE. If TRUE, the latent processes share the correlation and variance parameters. Otherwise, each latent process has distinct parameters.

kernel_type:

a character to specify the type of kernel to use. The current version supports kernel_type to be "matern_5_2" or "exponential", meaning that the matern kernel with roughness parameter being 2.5 or 0.5 (exponent kernel), respectively.

Methods

fit.gppca

See fit.gppca for details.

predict.gppca

See predict.gppca for details.

Author(s)

Mengyang Gu [aut, cre], Xinyi Fang [aut], Yizi Lin [aut]

Maintainer: Mengyang Gu <[e-mail]>

References

Gu, M., & Shen, W. (2020), Generalized probabilistic principal component analysis of correlated data, Journal of Machine Learning Research, 21(13), 1-41.

See Also

gppca for more details about how to create a gppca object.

FastGaSP documentation built on Aug. 27, 2025, 9:08 a.m.