print.MD {BsMD} | R Documentation |

## Print Best MD Follow-Up Experiments

### Description

Printing method for lists of class `MD`

. Displays the
best MD criterion set of runs and their MD for follow-up experiments.

### Usage

```
## S3 method for class 'MD'
print(x, X = FALSE, resp = FALSE, Xcand = TRUE, models = TRUE, nMod = x$nMod,
digits = 3, verbose=FALSE, ...)
```

### Arguments

`x` |
list of class |

`X` |
logical. If |

`resp` |
logical If |

`Xcand` |
logical. Prints the candidate runs if |

`models` |
logical. Competing models are printed if |

`nMod` |
integer. Top models to print. |

`digits` |
integer. Significant digits to use in the print out. |

`verbose` |
logical. If |

`...` |
additional arguments passed to |

### Value

The function is mainly called for its side effects. Prints out the selected
components of the class `MD`

objects, output of the `MD`

function.
For example the marginal factors and models posterior probabilities and
the top MD follow-up experiments with their corresponding MD statistic.
It returns invisible list with the components:

`calc` |
Numeric vector with basic calculation information. |

`models` |
Data frame with the competing models posterior probabilities. |

`follow-up` |
Data frame with the runs for follow-up experiments and their corresponding MD statistic. |

### Author(s)

Ernesto Barrios.

### References

Meyer, R. D., Steinberg, D. M. and Box, G. E. P. (1996). "Follow-Up Designs
to Resolve Confounding in Multifactor Experiments (with discussion)".
*Technometrics*, Vol. 38, No. 4, pp. 303–332.

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.

### See Also

### Examples

```
# Injection Molding Experiment. Meyer et al. 1996. Example 2.
# MD for one extra experiment.
library(BsMD)
data(BM93.e3.data,package="BsMD")
X <- as.matrix(BM93.e3.data[1:16,c(1,2,4,6,9)])
y <- BM93.e3.data[1:16,10]
nBlk <- 1
nFac <- 4
mInt <- 3
g <- 2
nMod <- 5
p <- c(0.2356,0.2356,0.2356,0.2356,0.0566)
s2 <- c(0.5815,0.5815,0.5815,0.5815,0.4412)
nf <- c(3,3,3,3,4)
facs <- matrix(c(2,1,1,1,1,3,3,2,2,2,4,4,3,4,3,0,0,0,0,4),nrow=5,
dimnames=list(1:5,c("f1","f2","f3","f4")))
nFDes <- 1
Xcand <- matrix(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
-1,-1,-1,-1,1,1,1,1,-1,-1,-1,-1,1,1,1,1,
-1,-1,1,1,-1,-1,1,1,-1,-1,1,1,-1,-1,1,1,
-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,
-1,1,1,-1,1,-1,-1,1,1,-1,-1,1,-1,1,1,-1),
nrow=16,dimnames=list(1:16,c("blk","f1","f2","f3","f4"))
)
mIter <- 0
startDes <- matrix(c(9,11,12,15),nrow=4)
top <- 10
injectionMolding.MD <- MD(X=X,y=y,nFac=nFac,nBlk=nBlk,mInt=mInt,g=g,
nMod=nMod,p=p,s2=s2,nf=nf,facs=facs,
nFDes=nFDes,Xcand=Xcand,mIter=mIter,startDes=startDes,top=top)
print(injectionMolding.MD)
summary(injectionMolding.MD)
```

*BsMD*version 2023.920 Index]