gppca-class {FastGaSP} | R Documentation |
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 isFALSE
. IfTRUE
, 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 tod
.shared_params
:object of class
logical
, default isTRUE
. IfTRUE
, 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 <mengyang@pstat.ucsb.edu>
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.