SSM-class: An S4 class to represent a smooth supersaturated model

Description Slots

Description

An S4 class to represent a smooth supersaturated model

Slots

dimension

A number indicating the number of variables in the design.

design

A matrix with rows indicating the design point and columns indicating the variable.

design_size

A number indicating the number of design points.

response

A design_size length vector of responses.

theta

A vector containing the fitted model coefficients.

basis

A matrix with each row being the exponent vector of a polynomial term.

basis_size

A number indicating the number of basis terms used in the model. This may be different from nrow(basis) if terms are excluded.

include

A vector containing the row numbers of the basis polynomials used in the model. This is used when interactions or variables are being excluded from the model.

K

A semi-positive definite matrix that defines the smoothing criteria.

P

A matrix that defines the polynomial basis in terms of a monomial basis.

design_model_matrix

A matrix.

variances

A vector of length basis_size containing the term variances.

total_variance

A length one vector containing the total variance.

main_sobol

A dimension length vector containing the Sobol index for each variable.

main_ind

A logical matrix indicating whether each term is included in the main effect corresponding to the column.

total_sobol

A dimension length vector containing the Total sensitivity index for each variable.

total_ind

A logical matrix indicating whether each term is included in the Total sensitivity index corresponding to the column.

int_sobol

A vector containing the Sobol index for interactions.

int_factors

A list of length the same as int_sobol indicating which interaction corresponds with each entry in int_sobol.

total_int

A vector containing the Total interaction indices of all second order interactions.

total_int_factors

A matrix where each row indicates the variables associated with the corresponding interaction in total_int.

distance

A matrix containing the distances used for computing the covariance matrix of the GP metamodel error estimate.

distance_type

A character defining the distance type used for computing distance. Can be one of "distance", "line", "product", "area", "proddiff", or "smoothdiff".

type

A character, either "exp", "matern32", that selects the correlation function used for the GP metamodel error estimate.

covariance

A positive definite matrix. The covariance matrix of the GP metamodel error estimate prior to scaling by sigma.

residuals

A design_size length vector containing the Leave-One-Out errors of the model at each design point.

sigma

A number indicating the scaling factor for covariance.

r

A number indicating the length factor for the correlation function.

local_smoothness

A design_size length vector containing the model smoothness at each design point.

LOO_RMSE

A number. The Leave-One-Out root mean square error.

legendre

logical. Indicates whether the default Legendre polynomial basis is being used.

fail

logical. Indicates whether the model fit was successful.


SSM documentation built on May 1, 2019, 10:09 p.m.