MeanZero: Zero Mean Function
In duckmayr/gpmss: Gaussian Process Models for Social Science
Description
Details
Super class
Public fields
Methods
Description
A zero mean; for any set of inputs, this function specifies that the
Gaussian process's mean is zero.
Details
As this function specifies a constant prior mean, it has no hyperparameters.
Super class
gpmss::MeanFunction -> MeanZero
Public fields
nameA character vector of length one giving the mean
function's name; "zero"
hypersA numeric vector giving the mean function's
hyperparameters
Methods
Public methods
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Inherited methods
Method mean()
Compute function prior mean
Usage
MeanZero$mean(X, hypers = NULL)
Arguments
XThe input values (should be a numeric matrix)
hypersA numeric vector giving hyperparameters for the
mean function. If NULL (the default), the hypers data
member is used.
Method parameter_derivative()
Compute partial derivatives of mean function with respect to
its hyperparameters
Usage
MeanZero$parameter_derivative(X, hypers = NULL, param = 1)
Arguments
XThe input values (should be a numeric matrix)
hypersA numeric vector giving hyperparameters for the
mean function. If NULL (the default), the hypers data
member is used.
paramAn integer vector of length one; which element of
hypers should the derivative be taken with respect to?
The default is 1
Method input_derivative()
Compute partial derivatives of mean function with respect to
its inputs
Usage
MeanZero$input_derivative(X, hypers = NULL, dimension = 1)
Arguments
XThe input values (should be a numeric matrix)
hypersA numeric vector giving hyperparameters for the
mean function. If NULL (the default), the hypers data
member is used.
dimensionan integer vector of length one giving the dimension
of X with respect to which the derivative is being taken; the
default is 1
Method new()
Create a new MeanZero object
Usage
MeanZero$new(hypers = numeric())
Arguments
hypersA(n empty) numeric vector giving hyperparameters for
the mean function.
Method clone()
The objects of this class are cloneable with this method.
Usage
MeanZero$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
duckmayr/gpmss documentation built on Nov. 8, 2021, 5:48 a.m.
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Description Details Super class Public fields Methods
Description
A zero mean; for any set of inputs, this function specifies that the Gaussian process's mean is zero.
Details
As this function specifies a constant prior mean, it has no hyperparameters.
Super class
gpmss::MeanFunction -> MeanZero
Public fields
nameA character vector of length one giving the mean function's name; "zero"
hypersA numeric vector giving the mean function's hyperparameters
Methods
Public methods
Inherited methods
Method mean()
Compute function prior mean
Usage
MeanZero$mean(X, hypers = NULL)
Arguments
XThe input values (should be a numeric matrix)
hypersA numeric vector giving hyperparameters for the mean function. If NULL (the default), the hypers data member is used.
Method parameter_derivative()
Compute partial derivatives of mean function with respect to its hyperparameters
Usage
MeanZero$parameter_derivative(X, hypers = NULL, param = 1)
Arguments
XThe input values (should be a numeric matrix)
hypersA numeric vector giving hyperparameters for the mean function. If NULL (the default), the hypers data member is used.
paramAn integer vector of length one; which element of
hypersshould the derivative be taken with respect to? The default is 1
Method input_derivative()
Compute partial derivatives of mean function with respect to its inputs
Usage
MeanZero$input_derivative(X, hypers = NULL, dimension = 1)
Arguments
XThe input values (should be a numeric matrix)
hypersA numeric vector giving hyperparameters for the mean function. If NULL (the default), the hypers data member is used.
dimensionan integer vector of length one giving the dimension of X with respect to which the derivative is being taken; the default is 1
Method new()
Create a new MeanZero object
Usage
MeanZero$new(hypers = numeric())
Arguments
hypersA(n empty) numeric vector giving hyperparameters for the mean function.
Method clone()
The objects of this class are cloneable with this method.
Usage
MeanZero$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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