Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/influence_functions.R

These functions calculate measures of the change in the covariance
matrices for the fixed effects based on the deletion of an
observation, or group of observations, for a hierarchical
linear model fit using `lmer`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Default S3 method:
covratio(object, ...)
## Default S3 method:
covtrace(object, ...)
## S3 method for class 'mer'
covratio(object, level = 1, delete = NULL, ...)
## S3 method for class 'lmerMod'
covratio(object, level = 1, delete = NULL, ...)
## S3 method for class 'lme'
covratio(object, level = 1, delete = NULL, ...)
## S3 method for class 'mer'
covtrace(object, level = 1, delete = NULL, ...)
## S3 method for class 'lmerMod'
covtrace(object, level = 1, delete = NULL, ...)
## S3 method for class 'lme'
covtrace(object, level = 1, delete = NULL, ...)
``` |

`object` |
fitted object of class |

`...` |
do not use |

`level` |
variable used to define the group for which cases will be
deleted. If |

`delete` |
index of individual cases to be deleted. To delete specific
observations the row number must be specified. To delete higher level
units the group ID and |

Both the covariance ratio (`covratio`

) and the covariance trace
(`covtrace`

) measure the change in the covariance matrix
of the fixed effects based on the deletion of a subset of observations.
The key difference is how the variance covariance matrices are compared:
`covratio`

compares the ratio of the determinants while `covtrace`

compares the trace of the ratio.

If `delete = NULL`

then a vector corresponding to each deleted
observation/group is returned.

If `delete`

is specified then a single value is returned corresponding
to the deleted subset specified.

Adam Loy loyad01@gmail.com

Christensen, R., Pearson, L., & Johnson, W. (1992)
Case-deletion diagnostics for mixed models. *Technometrics*, **34**(1),
38–45.

Schabenberger, O. (2004) Mixed Model Influence Diagnostics,
in *Proceedings of the Twenty-Ninth SAS Users Group International Conference*,
SAS Users Group International.

`leverage.mer`

, `cooks.distance.mer`

`mdffits.mer`

, `rvc.mer`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
data(sleepstudy, package = 'lme4')
ss <- lme4::lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)
# covratio for individual observations
ss.cr1 <- covratio(ss)
# covratio for subject-level deletion
ss.cr2 <- covratio(ss, level = "Subject")
## Not run:
## A larger example
data(Exam, package = 'mlmRev')
fm <- lme4::lmer(normexam ~ standLRT * schavg + (standLRT | school), data = Exam)
# covratio for individual observations
cr1 <- covratio(fm)
# covratio for school-level deletion
cr2 <- covratio(fm, level = "school")
## End(Not run)
# covtrace for individual observations
ss.ct1 <- covtrace(ss)
# covtrace for subject-level deletion
ss.ct2 <- covtrace(ss, level = "Subject")
## Not run:
## Returning to the larger example
# covtrace for individual observations
ct1 <- covtrace(fm)
# covtrace for school-level deletion
ct2 <- covtrace(fm, level = "school")
## End(Not run)
``` |

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