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

Provides index plots of influence and related diagnostics for a regression model.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
infIndexPlot(model, ...)
influenceIndexPlot(model, ...)
## S3 method for class 'lm'
infIndexPlot(model, vars=c("Cook", "Studentized", "Bonf", "hat"),
id=TRUE, grid=TRUE, main="Diagnostic Plots", ...)
## S3 method for class 'influence.merMod'
infIndexPlot(model,
vars = c("dfbeta", "dfbetas", "var.cov.comps",
"cookd"), id = TRUE, grid = TRUE, main = "Diagnostic Plots", ...)
## S3 method for class 'influence.lme'
infIndexPlot(model,
vars = c("dfbeta", "dfbetas", "var.cov.comps",
"cookd"), id = TRUE, grid = TRUE, main = "Diagnostic Plots", ...)
``` |

`model` |
A regression object of class |

`vars` |
All the quantities listed in this argument are plotted. Use |

`main` |
main title for graph |

`id` |
a list of named values controlling point labelling. The default, |

`grid` |
If TRUE, the default, a light-gray background grid is put on the graph. |

`...` |
Arguments passed to |

Used for its side effect of producing a graph. Produces index plots of diagnostic quantities.

Sanford Weisberg sandy@umn.edu and John Fox

Cook, R. D. and Weisberg, S. (1999)
*Applied Regression, Including Computing and Graphics.* Wiley.

Fox, J. (2016)
*Applied Regression Analysis and Generalized Linear Models*,
Third Edition. Sage.
Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

Weisberg, S. (2014)
*Applied Linear Regression*, Fourth Edition, Wiley.

`cooks.distance`

, `rstudent`

,
`outlierTest`

, `hatvalues`

, `influence.mixed.models`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
influenceIndexPlot(lm(prestige ~ income + education + type, Duncan))
## Not run: # a little slow
if (require(lme4)){
print(fm1 <- lmer(Reaction ~ Days + (Days | Subject),
sleepstudy)) # from ?lmer
infIndexPlot(influence(fm1, "Subject"))
infIndexPlot(influence(fm1))
}
if (require(lme4)){
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial) # from ?glmer
infIndexPlot(influence(gm1, "herd", maxfun=100))
infIndexPlot(influence(gm1, maxfun=100))
gm1.11 <- update(gm1, subset = herd != 11) # check deleting herd 11
compareCoefs(gm1, gm1.11)
}
## End(Not run)
``` |

```
Loading required package: carData
Loading required package: lme4
Loading required package: Matrix
Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
Linear mixed model fit by REML ['lmerMod']
Formula: Reaction ~ Days + (Days | Subject)
Data: sleepstudy
REML criterion at convergence: 1743.628
Random effects:
Groups Name Std.Dev. Corr
Subject (Intercept) 24.741
Days 5.922 0.07
Residual 25.592
Number of obs: 180, groups: Subject, 18
Fixed Effects:
(Intercept) Days
251.41 10.47
Calls:
1: glmer(formula = cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
2: glmer(formula = cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial, subset = herd != 11)
Model 1 Model 2
(Intercept) -1.398 -1.271
SE 0.231 0.240
period2 -0.992 -1.364
SE 0.303 0.343
period3 -1.128 -1.399
SE 0.323 0.354
period4 -1.580 -1.710
SE 0.422 0.453
```

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