Hald

hald

The Hald data have been used in many
books and papers to illustrate variable selection. The data relate to an
engineering application that was concerned with the effect of the
composition of cement on heat evolved during hardening. The response
variable Y is the heat evolved in a cement mix. The four
explanatory variables are ingredients of the mix, X1:
tricalcium aluminate, X2: tricalcium silicate, X3:
tetracalcium alumino ferrite, X4: dicalcium silicate. An
important feature of these data is that the variables X1 and
 X3 are highly correlated, as well as the variables X2 and 
X4. Thus we should expect any subset of (X1,X2,X3,X4) 
that includes one variable from highly correlated pair to do as any subset that 
also includes the other member.

datasets

Package for Bayesian Model Averaging in linear models
        using stochastic or deterministic sampling without replacement
        from posterior distributions. Prior distributions on
        coefficients are from Zellner's g-prior or mixtures of g-priors
        corresponding to the Zellner-Siow Cauchy Priors or the Liang et
        al hyper-g priors (JASA 2008). Other model selection criterian
        include AIC and BIC. Sampling probabilities may be updated
        based on the sampled models. Allows uniform or beta-binomial
        prior distributions on models.

Merlise Clyde

Bayesian Model Averaging using Bayesian Adaptive Sampling

Hald function

<code>hald</code> is a dataframe with 13 observations and 5 variables
 (columns), Y: Heat evolved per gram of cement (in calories)
 X1: Amount of tricalcium aluminate
 X2: Amount of tricalcium silicate
 X3: Amount of tetracalcium alumino ferrite
 X4: Amount of dicalcium silicate

format

Wood, H., Steinour, H.H., and Starke, H.R. (1932). "Effect of
  Composition of Portland cement on Heat Evolved During Hardening",
  Industrila and Engineering Chemistry, 24, 1207-1214.

Hald: Hald Data

Description

Usage

Arguments

format

source