R/Concrete.R

In MAVE: Methods for Dimension Reduction

#' Concrete Compressive Strength Data Set
#'
#' Concrete strength is very important in civil engineering and is a highly nonlinear function of age and
#' ingredients. This dataset contains 1030 instances and there are 8 features relevant to concrete strength.
#' The description of the varaibles are given below. The description is from \url{https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength}.

#' Name -- Data Type -- Measurement -- Description
#'
#' Cement (component 1) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Blast Furnace Slag (component 2) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Fly Ash (component 3) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Water (component 4) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Superplasticizer (component 5) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Coarse Aggregate (component 6) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Fine Aggregate (component 7) -- quantitative -- kg in a m3 mixture -- Input Variable
#'
#' Age -- quantitative -- Day (1~365) -- Input Variable
#'
#' Concrete compressive strength -- quantitative -- MPa -- Output Variable

#'
#'
#' @docType data
#' @keywords datasets
#' @format A data frame with 1030 rows and 8 covariate variables and 1 response variable
#' @source \url{https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength}
#' @references -Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998).
#' @name Concrete
#' @examples
#' data(Concrete)
#' train = sample(1:1030)[1:500]
#' x.train = as.matrix(Concrete[train,1:8])
#' y.train = as.matrix(Concrete[train,9])
#' x.test  = as.matrix(Concrete[-train,1:8])
#' y.test  = as.matrix(Concrete[-train,9])
#'
#' dr = mave.compute(x.train,y.train, method='meanopg',max.dim=8)
#' dr.dim = mave.dim(dr)
#' y.pred = predict(dr.dim,x.test)
#' #estimation error
#' mean((y.pred-y.test)^2)
#'

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