gppca-class | R Documentation |
An S4 class for generalized probabilistic principal component analysis of correlated data.
Objects of this class are created and initialized using the gppca
function to set up the estimation.
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.
See fit.gppca
for details.
See predict.gppca
for details.
Mengyang Gu [aut, cre], Xinyi Fang [aut], Yizi Lin [aut]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
Gu, M., & Shen, W. (2020), Generalized probabilistic principal component analysis of correlated data, Journal of Machine Learning Research, 21(13), 1-41.
gppca
for more details about how to create a gppca
object.
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