Probabilistic Utility

Probabilistic Utility

install.packages("mcmc")

> data(foo)
> out <- glm(y ~ x1 + x2 + x3, family = binomial, data = foo)
> summary(out)

Call:
glm(formula = y ~ x1 + x2 + x3, family = binomial, data = foo)

Deviance Residuals:

    Min       1Q   Median       3Q
-2.0371  -0.6337   0.2394   0.6685
    Max
 1.9599

Coefficients:
            Estimate Std. Error z value
(Intercept)   0.5772     0.2766   2.087
x1            0.3362     0.4256   0.790
x2            0.8475     0.4701   1.803
x3            1.5143     0.4426   3.422
            Pr(>|z|) 
(Intercept) 0.036930 *
x1          0.429672 
x2          0.071394 .
x3          0.000622 ***
---
Signif. codes:
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’
  0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 134.602  on 99  degrees of freedom
Residual deviance:  86.439  on 96  degrees of freedom
AIC: 94.439

Number of Fisher Scoring iterations: 5

> plot(out)
Hit <Return> to see next plot:
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