GP.summary {BayesGPfit} | R Documentation |

## Summary of posterior inference on the Bayesian Gaussian process regression model

### Description

Summary of posterior inference on the Bayesian Gaussian process regression model

### Usage

GP.summary(GP_fit)

### Arguments

### Value

A list object consisting of the following elements:

- mean
A list object for posterior mean of the target function,consisting of two elements (f is a vector for function values; x is
a vector or matrix for points evaluated).

- work_x
A matrix of real numbers for the standardized grid points for the model fitting.
It has the same dimension as "x".

- uci
A list object for 95% upper bound of the creditible interval
(uci) of the taget function,
consisting of two elements (f is a vector for function values;
x is a vector or matrix for points evaluated).

- lci
A list object for 95% lower bound of the creditibel interval (lci) of the taget function,
consisting of two elements (f is a vector for function values; x is
a vector or matrix for points evaluated).

- sigma2
A vector of posteror mean, the 95% lcl and ucl for
variance of the random error.

- tau2
A vector of posterior mean, the 95% lcl and ucl for
variance of the target function (hyperparameters).

### Author(s)

Jian Kang <jiankang@umich.edu>

### Examples

library(BayesGPfit)
library(lattice)
set.seed(1224)
dat = list()
dat$x = GP.generate.grids(d=2,num_grids = 30)
curve = GP.simulate.curve.fast(dat$x,a=0.01,b=0.5,poly_degree=20L)
dat$f = curve$f + rnorm(length(curve$f),sd=1)
fast_fit = GP.fast.Bayes.fit(dat$f,dat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE)
reg_fit = GP.Bayes.fit(dat$f,dat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE)
sum_fast_fit = GP.summary(fast_fit)
sum_reg_fit = GP.summary(reg_fit)
curves = list(mean_fast = sum_fast_fit$mean,
mean = sum_reg_fit$mean,
lci_fast = sum_fast_fit$lci,
lci = sum_reg_fit$lci,
uci_fast = sum_fast_fit$uci,
uci = sum_reg_fit$uci)
GP.plot.curves(curves,layout=c(2,3))

[Package

*BayesGPfit* version 0.1.0

Index]