knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
The SSM package provides functions to fit, plot and predict using smooth supersaturated models. It defines an S4 class called "SSM", and methods for plotting and predicting them. The fitting function is highly customizable and provides optional sensitivity analysis and the provision to estimate metamodel error using a Gaussian process.
The following code will fit a smooth supersaturated model to a 20 point design
in four factors. Note the design should be held in a matrix, not a data.frame,
and all entries must be numeric. The options SA
, GP
and validation
turn
on automated sensitivity analysis, Gaussian process metamodel error estimation
and Leave-One-Out cross-validation respectively. The plot
method plots the
main effects of the model while the predict
method gives the model prediction
at a point and also a 95% credible interval if a metamodel error GP has been
fit.
X <- matrix(runif(80, -1, 1), ncol = 4) Y <- apply(apply(X, 1, "^", 1:4), 2, sum) s <- fit.ssm(X, Y, SA = TRUE, GP = TRUE, validation = TRUE) s plot(s, yrange="yrange") predict(s, rep(0.5, 4)) sensitivity.plot(s)
To install the most up-to-date SSM package through GitHub use
devtools::install_github("peterrobertcurtis/SSM")
.
More details on how to use the SSM can be found in the vignette and help pages.
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