BsProb1: Posterior Probabilities from Bayesian Screening Experiments

In ana-vela7/BsMD2: Bayes Screening and Model Discrimination

Description

Marginal factor posterior probabilities and model posterior probabilities from designed screening experiments are calculated according to Box and Meyer's Bayesian procedure.

Usage

BsProb1(X, y, p = 0.25, gamma = 2, max_int = 3, max_fac = ncol(X), top = 10)

Arguments

X	Matrix. The design matrix.
y	Vector. The response vector.
p	Numeric. Prior probability assigned to active factors.
gamma	Numeric. Variance inflation associated to active factors.
max_int	Integer <= 3. Maximum order of interactions considered in the models.
max_fac	Integer. Maximum number of factors included in the models.
top	Integer. Number of models to keep with the highest posterior probability.

Value

A list with all the input and output parameters.

X	Matrix. The design matrix.
y	Vector. The response vector.
n	Integer. Number of runs.
col	Integer. Number of columns in the design matrix.
max_int	Integer <= 3. Maximum order of interactions considered in the models.
max_fac	Integer. Maximum number of factors included in the models.
pi	Numeric. Prior probability assigned to active factors.
gamma	Numeric. Variance inflation associated with active factors.
top	Integer. Number of models to keep with the highest posterior probability.
Prob_fac	Data frame. Posterior probability for each factor.
Prob_mod	Data frame. Posterior probability for each of the top models.
nfac_mod	Vector. Number of active factors in each of the top models.
p_mod	Vector. Posterior probability for each of the top models.
fac_mod	Matrix. Active factors for each of the top models.

References

Box, G. E. P and R. D. Meyer (1986). "An Analysis for Unreplicated Fractional Factorials". Technometrics. Vol. 28. No. 1. pp. 11–18.

Box, G. E. P and R. D. Meyer (1993). "Finding the Active Factors in Fractionated Screening Experiments". Journal of Quality Technology. Vol. 25. No. 2. pp. 94–105.

Examples

#Example 1
library(BsMD2)
data("BM93e1")
X <- as.matrix(BM93e1[,2:6])
y <- BM93e1[,7]
drillAdvance.BsProb1 <- BsMD2::BsProb1(X, y, .25, 1.6, 3, 5)
plot(drillAdvance.BsProb1)
summary(drillAdvance.BsProb1)

#Example 2
data("BM93e2")
X <- as.matrix(BM93e2[,1:7])
y <- BM93e2[,8]
pp <- BsMD2::BsProb1(X, y, .25, 1.5, 3, 7)
plot(pp)
summary(pp)

#Example 3
data("BM93e3")
X16 <- as.matrix(BM93e3[1:16,2:9])
y16 <- BM93e3[1:16,10]
pp16 <- BsMD2::BsProb1(X16, y16, .25, 2, 3, 8)

X <- as.matrix(BM93e3[,1:9])
y <- BM93e3[,10]
pp <- BsProb1(X, y, .25, 2, 3, 4)

ana-vela7/BsMD2 documentation built on Dec. 19, 2021, 2:32 a.m.