Kriging Models using the 'libKriging' Library

---

Documentation for package ‘rlibkriging’ version 0.8-0.1

• DESCRIPTION file.

Help Pages

A C F K L N P S T U V X Y misc

-- A --

abline(v	Compute Log-Likelihood of NoiseKriging Model
as.km	Coerce an Object into a 'km' Object
as.km-method	Coerce a 'Kriging' object into the '"km"' class of the 'DiceKriging' package.
as.km.Kriging	Coerce a 'Kriging' object into the '"km"' class of the 'DiceKriging' package.
as.km.NoiseKriging	Coerce a 'NoiseKriging' object into the '"km"' class of the 'DiceKriging' package.
as.km.NuggetKriging	Coerce a 'NuggetKriging' object into the '"km"' class of the 'DiceKriging' package.
as.list-method	Coerce a 'Kriging' Object into a List
as.list-method	Coerce a 'NoiseKriging' Object into a List
as.list-method	Coerce a 'NuggetKriging' Object into a List
as.list.Kriging	Coerce a 'Kriging' Object into a List
as.list.NoiseKriging	Coerce a 'NoiseKriging' Object into a List
as.list.NuggetKriging	Coerce a 'NuggetKriging' Object into a List
as.matrix(runif(10))	Compute Log-Likelihood of NoiseKriging Model

-- C --

cbind(theta,sigma20))$logLikelihood	Compute Log-Likelihood of NoiseKriging Model
cbind(theta0,sigma2))$logLikelihood	Compute Log-Likelihood of NoiseKriging Model
col	Compute Log-Likelihood of NoiseKriging Model
contour(t,s2,matrix(ncol=length(s2),ll(expand.grid(t,s2))),xlab="theta",ylab="sigma2")	Compute Log-Likelihood of NoiseKriging Model
copy	Duplicate object.
copy-method	Duplicate a Kriging Model
copy-method	Duplicate a NoiseKriging Model
copy-method	Duplicate a NuggetKriging Model
copy.Kriging	Duplicate a Kriging Model
copy.NoiseKriging	Duplicate a NoiseKriging Model
copy.NuggetKriging	Duplicate a NuggetKriging Model
cos(7	Compute Log-Likelihood of NoiseKriging Model

-- F --

f	Compute Log-Likelihood of NoiseKriging Model
f(X)	Compute Log-Likelihood of NoiseKriging Model
fit	Fit model on data.
fit.Kriging	Fit 'Kriging' object on given data.
fit.NoiseKriging	Fit 'NoiseKriging' object on given data.
fit.NuggetKriging	Fit 'NuggetKriging' object on given data.
function(sigma2)	Compute Log-Likelihood of NoiseKriging Model
function(theta)	Compute Log-Likelihood of NoiseKriging Model
function(theta_sigma2)	Compute Log-Likelihood of NoiseKriging Model
function(x)	Compute Log-Likelihood of NoiseKriging Model

-- K --

k	Compute Log-Likelihood of NoiseKriging Model
k$sigma2()	Compute Log-Likelihood of NoiseKriging Model
k$sigma2(),	Compute Log-Likelihood of NoiseKriging Model
k$theta()	Compute Log-Likelihood of NoiseKriging Model
k$theta(),	Compute Log-Likelihood of NoiseKriging Model
kernel	Compute Log-Likelihood of NoiseKriging Model
KM	Create an 'KM' Object
KM-class	S4 class for Kriging Models Extending the '"km"' Class
Kriging	Create an object with S3 class '"Kriging"' using the 'libKriging' library.

-- L --

leaveOneOut	Compute Leave-One-Out
leaveOneOut-method	Get leaveOneOut of Kriging Model
leaveOneOut.Kriging	Get leaveOneOut of Kriging Model
leaveOneOutFun	Leave-One-Out function
leaveOneOutFun-method	Compute Leave-One-Out (LOO) error for an object with S3 class '"Kriging"' representing a kriging model.
leaveOneOutFun.Kriging	Compute Leave-One-Out (LOO) error for an object with S3 class '"Kriging"' representing a kriging model.
leaveOneOutVec	Leave-One-Out vector
leaveOneOutVec-method	Compute Leave-One-Out (LOO) vector error for an object with S3 class '"Kriging"' representing a kriging model.
leaveOneOutVec.Kriging	Compute Leave-One-Out (LOO) vector error for an object with S3 class '"Kriging"' representing a kriging model.
length.out	Compute Log-Likelihood of NoiseKriging Model
ll	Compute Log-Likelihood of NoiseKriging Model
ll_sigma2	Compute Log-Likelihood of NoiseKriging Model
ll_theta	Compute Log-Likelihood of NoiseKriging Model
load	Load any Kriging Model from a file storage.
load.Kriging	Load a Kriging Model from a file storage
load.NoiseKriging	Load a NoiseKriging Model from a file storage
load.NuggetKriging	Load a NuggetKriging Model from a file storage
logLikelihood	Compute Log-Likelihood
logLikelihood-method	Get Log-Likelihood of Kriging Model
logLikelihood-method	Get logLikelihood of NoiseKriging Model
logLikelihood-method	Get logLikelihood of NuggetKriging Model
logLikelihood.Kriging	Get Log-Likelihood of Kriging Model
logLikelihood.NoiseKriging	Get logLikelihood of NoiseKriging Model
logLikelihood.NuggetKriging	Get logLikelihood of NuggetKriging Model
logLikelihoodFun	Log-Likelihood function
logLikelihoodFun(k,	Compute Log-Likelihood of NoiseKriging Model
logLikelihoodFun-method	Compute Log-Likelihood of Kriging Model
logLikelihoodFun-method	Compute Log-Likelihood of NoiseKriging Model
logLikelihoodFun-method	Compute Log-Likelihood of NuggetKriging Model
logLikelihoodFun.Kriging	Compute Log-Likelihood of Kriging Model
logLikelihoodFun.NoiseKriging	Compute Log-Likelihood of NoiseKriging Model
logLikelihoodFun.NuggetKriging	Compute Log-Likelihood of NuggetKriging Model
logMargPost	Compute log-Marginal Posterior
logMargPost-method	Get logMargPost of Kriging Model
logMargPost-method	Get logMargPost of NuggetKriging Model
logMargPost.Kriging	Get logMargPost of Kriging Model
logMargPost.NuggetKriging	Get logMargPost of NuggetKriging Model
logMargPostFun	log-Marginal Posterior function
logMargPostFun-method	Compute the log-marginal posterior of a kriging model, using the prior XXXY.
logMargPostFun-method	Compute the log-marginal posterior of a kriging model, using the prior XXXY.
logMargPostFun.Kriging	Compute the log-marginal posterior of a kriging model, using the prior XXXY.
logMargPostFun.NuggetKriging	Compute the log-marginal posterior of a kriging model, using the prior XXXY.

-- N --

NoiseKM	Create an 'NoiseKM' Object
NoiseKM-class	S4 class for NoiseKriging Models Extending the '"km"' Class
NoiseKriging	Create an object with S3 class '"NoiseKriging"' using the 'libKriging' library.
NoiseKriging(y,	Compute Log-Likelihood of NoiseKriging Model
NuggetKM	Create an 'NuggetKM' Object
NuggetKM-class	S4 class for NuggetKriging Models Extending the '"km"' Class
NuggetKriging	Create an object with S3 class '"NuggetKriging"' using the 'libKriging' library.

-- P --

plot(s2,	Compute Log-Likelihood of NoiseKriging Model
plot(t,	Compute Log-Likelihood of NoiseKriging Model
points(k$theta(),k$sigma2(),col='blue')	Compute Log-Likelihood of NoiseKriging Model
predict-method	Prediction Method for a 'KM' Object
predict-method	Prediction Method for a 'NoiseKM' Object
predict-method	Prediction Method for a 'NuggetKM' Object
predict.Kriging	Predict from a 'Kriging' object.
predict.NoiseKriging	Predict from a 'NoiseKriging' object.
predict.NuggetKriging	Predict from a 'NuggetKriging' object.
print(k)	Compute Log-Likelihood of NoiseKriging Model
print.Kriging	Print the content of a 'Kriging' object.
print.NoiseKriging	Print the content of a 'NoiseKriging' object.
print.NuggetKriging	Print the content of a 'NuggetKriging' object.

-- S --

s2	Compute Log-Likelihood of NoiseKriging Model
save	Save object.
save-method	Save a Kriging Model to a file storage
save-method	Save a NoiseKriging Model to a file storage
save-method	Save a NuggetKriging Model to a file storage
save.Kriging	Save a Kriging Model to a file storage
save.NoiseKriging	Save a NoiseKriging Model to a file storage
save.NuggetKriging	Save a NuggetKriging Model to a file storage
seq(from	Compute Log-Likelihood of NoiseKriging Model
set.seed(123)	Compute Log-Likelihood of NoiseKriging Model
sigma20	Compute Log-Likelihood of NoiseKriging Model
simulate-method	Simulation from a 'KM' Object
simulate-method	Simulation from a 'NoiseKM' Object
simulate-method	Simulation from a 'NuggetKM' Object
simulate.Kriging	Simulation from a 'Kriging' model object.
simulate.NoiseKriging	Simulation from a 'NoiseKriging' model object.
simulate.NuggetKriging	Simulation from a 'NuggetKriging' model object.

-- T --

t	Compute Log-Likelihood of NoiseKriging Model
theta0	Compute Log-Likelihood of NoiseKriging Model
theta_sigma2)$logLikelihood	Compute Log-Likelihood of NoiseKriging Model
to	Compute Log-Likelihood of NoiseKriging Model
type	Compute Log-Likelihood of NoiseKriging Model

-- U --

update-method	Update a 'KM' Object with New Points
update-method	Update a 'NoiseKM' Object with New Points
update-method	Update a 'NuggetKM' Object with New Points
update.Kriging	Update a 'Kriging' model object with new points
update.NoiseKriging	Update a 'NoiseKriging' model object with new points
update.NuggetKriging	Update a 'NuggetKriging' model object with new points

-- V --

Vectorize(ll_sigma2)(s2),	Compute Log-Likelihood of NoiseKriging Model
Vectorize(ll_theta)(t),	Compute Log-Likelihood of NoiseKriging Model

-- X --

X	Compute Log-Likelihood of NoiseKriging Model
x)	Compute Log-Likelihood of NoiseKriging Model
X,	Compute Log-Likelihood of NoiseKriging Model
X/10	Compute Log-Likelihood of NoiseKriging Model
x^5	Compute Log-Likelihood of NoiseKriging Model

-- Y --

y	Compute Log-Likelihood of NoiseKriging Model

-- misc --

"blue")	Compute Log-Likelihood of NoiseKriging Model
"matern3_2")	Compute Log-Likelihood of NoiseKriging Model
'l')	Compute Log-Likelihood of NoiseKriging Model
(1	Compute Log-Likelihood of NoiseKriging Model
(sin(12	Compute Log-Likelihood of NoiseKriging Model
(X/10)^2,	Compute Log-Likelihood of NoiseKriging Model
*	Compute Log-Likelihood of NoiseKriging Model
*rnorm(nrow(X))	Compute Log-Likelihood of NoiseKriging Model
+	Compute Log-Likelihood of NoiseKriging Model
-	Compute Log-Likelihood of NoiseKriging Model
/	Compute Log-Likelihood of NoiseKriging Model
0.001,	Compute Log-Likelihood of NoiseKriging Model
0.7)	Compute Log-Likelihood of NoiseKriging Model
1	Compute Log-Likelihood of NoiseKriging Model
1,	Compute Log-Likelihood of NoiseKriging Model
101)	Compute Log-Likelihood of NoiseKriging Model
2	Compute Log-Likelihood of NoiseKriging Model
2,	Compute Log-Likelihood of NoiseKriging Model
31)	Compute Log-Likelihood of NoiseKriging Model
<-	Compute Log-Likelihood of NoiseKriging Model
=	Compute Log-Likelihood of NoiseKriging Model