Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google

Tutorial Example

to again use the binomial distribution, but to specify the probit link function (identified as binomial(probit)). The probit function



> mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/logit.csv"))
> myprobit<- glm(admit ~ gre + gpa + topnotch, data=mydata,
                 family=binomial(link="probit"),  na.action=na.pass)
> summary(myprobit)

Call:
glm(formula = admit ~ gre + gpa + topnotch, family = binomial(link = "probit"), 
    data = mydata, na.action = na.pass)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3862  -0.8856  -0.7130   1.2715   1.9800  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.7978830  0.6476352  -4.320 1.56e-05 ***
gre          0.0015244  0.0006404   2.380   0.0173 *  
gpa          0.4009847  0.1948032   2.058   0.0396 *  
topnotch     0.2730331  0.1803284   1.514   0.1300    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 499.98  on 399  degrees of freedom
Residual deviance: 477.89  on 396  degrees of freedom
AIC: 485.89

Number of Fisher Scoring iterations: 4



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