The {car} R package - The Prestige Data set

avPlots(reg1)

• id.n–id most influential observation
• id.cex –font size for id.
• Graphs outcomevspredictor variables holding the rest constant (also called partial-regression plots)
• Help identify the effect(or influence) of an observation on the regression coefficient of the predictor variable

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influenceIndexPlot(reg1)

• Cook's distance measures how much an observation influences the overall model or predicted values
• Studentizidedresiduals are the residuals divided by their estimated standard deviation as a way to standardized
• Bonferronitest to identify outliers
• Hat-points identify influential observations (have a high impact on the predictor variables) NOTE: If an observation is an outlier and influential (high leverage) then that observation can change the fit of the linear model, it is advisable to remove it. To remove a case(s) type

# reg1a <-update(prestige.reg4, subset=rownames(Prestige) != "general.managers")
# reg1b <-update(prestige.reg4, subset= !(rownames(Prestige) %in% c("general.managers","medical.technicians")))

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influencePlot(reg1)

##                        StudRes        Hat       CookD
## general.managers    -1.3134574 0.33504477 0.172503975
## physicians          -0.3953204 0.22420309 0.009115491
## medical.technicians  2.8210910 0.06858836 0.109052582
## electronic.workers   2.2251940 0.02701237 0.026372394

• Creates a bubble-plot combining the display of Studentizedresiduals, hat-values, and Cook's distance (represented in the circles).

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qqPlot(reg1)

## medical.technicians  electronic.workers 
##                  31                  82

Look for the tails, points should be close to the line or within the confidence intervals.

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