kernel_latentFactor_dC: Derivative of covariance matrix of X with respect to kernel...

In CollinErickson/GauPro: Gaussian Process Fitting

Derivative of covariance matrix of X with respect to kernel parameters for the Latent Factor Kernel

Description

Derivative of covariance matrix of X with respect to kernel parameters for the Latent Factor Kernel

Usage

kernel_latentFactor_dC(
  x,
  pf,
  C_nonug,
  s2_est,
  p_est,
  lenparams_D,
  s2_nug,
  latentdim,
  xindex,
  nlevels,
  s2
)

Arguments

x	Matrix x
pf	pf vector
C_nonug	cov mat without nugget
s2_est	whether s2 is being estimated
p_est	Whether theta/beta is being estimated
lenparams_D	Number of parameters the derivative is being calculated for
s2_nug	s2 times the nug
latentdim	Number of latent dimensions
xindex	Which column of x is the indexing variable
nlevels	Number of levels
s2	Value of s2

Value

Correlation matrix

CollinErickson/GauPro documentation built on March 25, 2024, 11:20 p.m.