SSM-class {SSM}

An S4 class to represent a smooth supersaturated model

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.