bandle-gp: Compute GP gradient

In ococrook/bandle: An R package for the Bayesian analysis of differential subcellular localisation experiments

Compute GP gradient

Description

Internal R function to pass R to C++, not for external use.

Internal R function to pass R to C++, not for external use.

Function to perform Metropolis-Hastings for GP hyperparameters with different priors

Usage

gradientGP(Xk, tau, h, nk, D)

gradientGPmatern(Xk, tau, h, nk, D, materncov, nu)

posteriorgradientGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar)

gradientlogprior(h, hyppar)

likelihoodGP(Xk, tau, h, nk, D)

likelihoodGPmatern(Xk, tau, h, nk, D, materncov, nu)

posteriorGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar)

Gumbel(x, lambda, log = TRUE)

PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)

metropolisGP(
  inith,
  X,
  tau,
  nk,
  D,
  niter,
  hyperMean = c(0, 0, 0),
  hyperSd = c(1, 1, 1)
)

metropolisGPmatern(
  inith,
  X,
  tau,
  nk,
  D,
  niter,
  nu = 2,
  hyppar = c(1, 1, 1),
  propSd = c(0.3, 0.1, 0.1)
)

Gumbel(x, lambda, log = TRUE)

PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)

Arguments

Xk	The data
tau	The indexing parameters
h	GP hyperparameters
nk	Number of observations
D	number of samples
materncov	logical indicating whether matern covariance is used
nu	Smoothness of the matern covariance
hyppar	A vector indicating the penalised complexity prior hyperparameters. Default is c(1,1,1)
x	observation
lambda	scale parameter of the type-2 Gumbel distribution
log	logical indicating whether to return log. Default is TRUE
rho	length-scale parameter
a	amplitude
lambda1	first parameter of distribution
lambda2	second parameter of distribution
inith	initial hyperparamters
X	The data
niter	Number of MH iteractions
hyperMean	A vector indicating the log-normal means. Default is c(0,0,0).
hyperSd	A vector indicating the log-normal standard deviations. Default is c(1,1,1)
propSd	The proposal standard deviation. Default is c(0.3,0.1,0.1). Do not change unless you know what you are doing.

Value

Returns gp gradient

Returns gp gradient

Returns the gradient of the posterior

return the gradient of the log prior, length-scale, aamplitude and noise

Returns gp negative log likelihood

Returns gp negative log likelihood

Returns the negative log posterior of the GP

Returns the likelihood of the type-2 GUmbel distribution

Returns the likelihood of the bivariate penalised complexity prior

Returns new hyperparamters and the acceptance rate

Returns the likelihood of the type-2 GUmbel distribution

Returns the likelihood of the bivariate penalised complexity prior

Examples

Gumbel(3, lambda = 1)

ococrook/bandle documentation built on Nov. 4, 2024, 12:27 a.m.