Output from berkeley-diag.R

# diagnostic plots for berkeley data
library(car)

# using glm()
berkeley <- as.data.frame(UCBAdmissions)
cellID <- paste(berkeley$Dept, substr(berkeley$Gender, 1, 1), "-", substr(berkeley$Admit, 1, 
    3), sep = "")
rownames(berkeley) <- cellID

berk.mod <- glm(Freq ~ Dept * (Gender + Admit), data = berkeley, family = "poisson")
summary(berk.mod)

## 
## Call:
## glm(formula = Freq ~ Dept * (Gender + Admit), family = "poisson", 
##     data = berkeley)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -3.478  -0.414   0.010   0.309   2.232  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)    
## (Intercept)           6.2756     0.0425  147.74  < 2e-16 ***
## DeptB                -0.4057     0.0677   -5.99  2.1e-09 ***
## DeptC                -1.5394     0.0831  -18.54  < 2e-16 ***
## DeptD                -1.3223     0.0816  -16.21  < 2e-16 ***
## DeptE                -2.4028     0.1101  -21.82  < 2e-16 ***
## DeptF                -3.0962     0.1576  -19.65  < 2e-16 ***
## GenderFemale         -2.0333     0.1023  -19.87  < 2e-16 ***
## AdmitRejected        -0.5935     0.0684   -8.68  < 2e-16 ***
## DeptB:GenderFemale   -1.0758     0.2286   -4.71  2.5e-06 ***
## DeptC:GenderFemale    2.6346     0.1234   21.35  < 2e-16 ***
## DeptD:GenderFemale    1.9271     0.1246   15.46  < 2e-16 ***
## DeptE:GenderFemale    2.7548     0.1351   20.39  < 2e-16 ***
## DeptF:GenderFemale    1.9436     0.1268   15.32  < 2e-16 ***
## DeptB:AdmitRejected   0.0506     0.1097    0.46     0.64    
## DeptC:AdmitRejected   1.2091     0.0973   12.43  < 2e-16 ***
## DeptD:AdmitRejected   1.2583     0.1015   12.40  < 2e-16 ***
## DeptE:AdmitRejected   1.6830     0.1173   14.34  < 2e-16 ***
## DeptF:AdmitRejected   3.2691     0.1671   19.57  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 2650.095  on 23  degrees of freedom
## Residual deviance:   21.736  on  6  degrees of freedom
## AIC: 216.8
## 
## Number of Fisher Scoring iterations: 4

influencePlot(berk.mod, labels = cellID, id.n = 3)

##        StudRes    Hat  CookD
## AM-Adm -4.1541 0.9588 4.7228
## AM-Rej  4.1498 0.9254 3.4486
## AF-Adm  4.0992 0.6853 1.4447
## AF-Rej -4.4178 0.4304 0.8509
## BM-Adm -0.5037 0.9843 0.9399
## BM-Rej  0.5037 0.9730 0.7123
## FM-Rej  0.6197 0.9692 0.8199

plot(berk.mod)

## Warning: NaNs produced

## Warning: NaNs produced