# Regression Deletion Diagnostics for Multivariate Linear Models

### Description

This collection of functions is designed to compute regression deletion
diagnostics for multivariate linear models following Barrett & Ling (1992)
that are close analogs of
methods for univariate and generalized linear models handled by the
`influence.measures`

in the stats package.

In addition, the functions provide diagnostics for deletion of
subsets of observations of size `m>1`

.

### Usage

1 2 3 4 5 6 7 8 |

### Arguments

`model` |
An |

`do.coef` |
logical. Should the coefficients be returned in the |

`m` |
Size of the subsets for deletion diagnostics |

`x` |
An |

`FUN` |
For |

`funnames` |
logical. Should the |

`...` |
Other arguments passed to methods |

`digits` |
Number of digits for the print method |

### Details

`influence.mlm`

is a simple wrapper for the computational function, `mlm.influence`

designed to provide an S3 method for class `"mlm"`

objects.

There are still infelicities in the methods for the `m>1`

case in the current implementation.
In particular, for `m>1`

, you must call `influence.mlm`

directly, rather than using
the S3 generic `influence()`

.

### Value

`influence.mlm`

returns an S3 object of class `inflmlm`

, a list with the following components

`m` |
Deletion subset size |

`H` |
Hat values, |

`Q` |
Residuals, |

`CookD` |
Cook's distance values |

`L` |
Leverage components |

`R` |
Residual components |

`subsets` |
Indices of the observations in the subsets of size |

`labels` |
Observation labels |

`call` |
Model call for the |

`Beta` |
Deletion regression coefficients– included if |

### Author(s)

Michael Friendly

### References

Barrett, B. E. and Ling, R. F. (1992).
General Classes of Influence Measures for Multivariate Regression.
*Journal of the American Statistical Association*, **87**(417), 184-191.

### See Also

`influencePlot.mlm`

, `mlm.influence`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# Rohwer data
Rohwer2 <- subset(Rohwer, subset=group==2)
rownames(Rohwer2)<- 1:nrow(Rohwer2)
Rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ n+s+ns+na+ss, data=Rohwer2)
# m=1 diagnostics
influence(Rohwer.mod)
# try an m=2 case
res2 <- influence.mlm(Rohwer.mod, m=2, do.coef=FALSE)
res2.df <- as.data.frame(res2)
head(res2.df)
scatterplotMatrix(log(res2.df))
influencePlot(Rohwer.mod, id.n=4, type="cookd")
# Sake data
Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)
influence(Sake.mod)
influencePlot(Sake.mod, id.n=3, type="cookd")
``` |