predict {deepgp}  R Documentation 
Acts on a gp
, dgp2
, or dgp3
object.
Calculates posterior mean and variance/covariance over specified input
locations. Optionally calculates expected improvement (EI) over
candidate inputs. Optionally utilizes SNOW parallelization.
## S3 method for class 'gp'
predict(object, x_new, lite = TRUE, EI = FALSE, cores = detectCores()  1, ...)
## S3 method for class 'dgp2'
predict(
object,
x_new,
lite = TRUE,
store_latent = FALSE,
mean_map = TRUE,
EI = FALSE,
cores = detectCores()  1,
...
)
## S3 method for class 'dgp3'
predict(
object,
x_new,
lite = TRUE,
store_latent = FALSE,
mean_map = TRUE,
EI = FALSE,
cores = detectCores()  1,
...
)
## S3 method for class 'gpvec'
predict(
object,
x_new,
m = object$m,
lite = TRUE,
cores = detectCores()  1,
...
)
## S3 method for class 'dgp2vec'
predict(
object,
x_new,
m = object$m,
lite = TRUE,
store_latent = FALSE,
mean_map = TRUE,
cores = detectCores()  1,
...
)
## S3 method for class 'dgp3vec'
predict(
object,
x_new,
m = object$m,
lite = TRUE,
store_latent = FALSE,
mean_map = TRUE,
cores = detectCores()  1,
...
)
object 
object from 
x_new 
matrix of predictive input locations 
lite 
logical indicating whether to calculate only pointwise
variances ( 
EI 
logical indicating whether to calculate expected improvement (for minimizing the response) 
cores 
number of cores to utilize in parallel, defaults to available cores minus one 
... 
N/A 
store_latent 
logical indicating whether to store and return mapped values of latent layers (two or three layer models only) 
mean_map 
logical indicating whether to map hidden layers using
conditional mean ( 
m 
size of Vecchia conditioning sets (only for fits with

All iterations in the object are used for prediction, so samples
should be burnedin. Thinning the samples using trim
will speed
up computation. Posterior moments are calculated using conditional
expectation and variance. As a default, only pointwise variance is
calculated. Full covariance may be calculated using lite = FALSE
.
Expected improvement is calculated with the goal of minimizing the response. See Chapter 7 of Gramacy (2020) for details.
SNOW parallelization reduces computation time but requires more memory storage.
object of the same class with the following additional elements:
x_new
: copy of predictive input locations
mean
: predicted posterior mean, indices correspond to
x_new
locations
s2
: predicted pointwise variances, indices correspond to
x_new
locations (only returned when lite = TRUE
)
s2_smooth
: predicted pointwise variances with g
removed, indices correspond to x_new
locations (only returned
when lite = TRUE
)
Sigma
: predicted posterior covariance, indices correspond to
x_new
locations (only returned when lite = FALSE
)
Sigma_smooth
: predicted posterior covariance with g
removed from the diagonal (only returned when lite = FALSE
)
EI
: vector of expected improvement values, indices correspond
to x_new
locations (only returned when EI = TRUE
)
w_new
: list of hidden layer mappings (only returned when
store_latent = TRUE
), list index corresponds to iteration and
row index corresponds to x_new
location (two or three layer
models only)
z_new
: list of hidden layer mappings (only returned when
store_latent = TRUE
), list index corresponds to iteration and
row index corresponds to x_new
location (three layer models only)
Computation time is added to the computation time of the existing object.
Sauer, A, RB Gramacy, and D Higdon. 2020. "Active Learning for Deep Gaussian
Process Surrogates." Technometrics, to appear; arXiv:2012.08015.
Sauer, A, A Cooper, and RB Gramacy. 2022. "Vecchiaapproximated Deep Gaussian
Processes for Computer Experiments." preprint on arXiv:2204.02904
Gramacy, RB. Surrogates: Gaussian Process Modeling, Design, and
Optimization for the Applied Sciences. Chapman Hall, 2020.
# See "fit_one_layer", "fit_two_layer", or "fit_three_layer"
# for an example