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Logit example.html
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<h3>Logit example.R</h3>
<p>shill — <em>May 7, 2013, 10:04 AM</em></p>
<pre><code class="r">data(Affairs, package="AER")
summary(Affairs)
</code></pre>
<pre><code> affairs gender age yearsmarried children
Min. : 0.00 female:315 Min. :17.5 Min. : 0.125 no :171
1st Qu.: 0.00 male :286 1st Qu.:27.0 1st Qu.: 4.000 yes:430
Median : 0.00 Median :32.0 Median : 7.000
Mean : 1.46 Mean :32.5 Mean : 8.178
3rd Qu.: 0.00 3rd Qu.:37.0 3rd Qu.:15.000
Max. :12.00 Max. :57.0 Max. :15.000
religiousness education occupation rating
Min. :1.00 Min. : 9.0 Min. :1.0 Min. :1.00
1st Qu.:2.00 1st Qu.:14.0 1st Qu.:3.0 1st Qu.:3.00
Median :3.00 Median :16.0 Median :5.0 Median :4.00
Mean :3.12 Mean :16.2 Mean :4.2 Mean :3.93
3rd Qu.:4.00 3rd Qu.:18.0 3rd Qu.:6.0 3rd Qu.:5.00
Max. :5.00 Max. :20.0 Max. :7.0 Max. :5.00
</code></pre>
<pre><code class="r">table(Affairs$affairs)
</code></pre>
<pre><code>
0 1 2 3 7 12
451 34 17 19 42 38
</code></pre>
<pre><code class="r">Affairs$ynaffair[Affairs$affairs > 0] <- 1
Affairs$ynaffair[Affairs$affairs == 0] <- 0
Affairs$ynaffair <- factor(Affairs$ynaffair,levels=c(0,1),labels=c("No","Yes"))
table(Affairs$ynaffair)
</code></pre>
<pre><code>
No Yes
451 150
</code></pre>
<pre><code class="r">fit.full <- glm(ynaffair ~ gender + age + yearsmarried + children +religiousness +
education + occupation +rating, data=Affairs,family=binomial());
summary(fit.full)
</code></pre>
<pre><code>
Call:
glm(formula = ynaffair ~ gender + age + yearsmarried + children +
religiousness + education + occupation + rating, family = binomial(),
data = Affairs)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.571 -0.750 -0.569 -0.254 2.519
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.3773 0.8878 1.55 0.1208
gendermale 0.2803 0.2391 1.17 0.2411
age -0.0443 0.0182 -2.43 0.0153 *
yearsmarried 0.0948 0.0322 2.94 0.0033 **
childrenyes 0.3977 0.2915 1.36 0.1725
religiousness -0.3247 0.0898 -3.62 0.0003 ***
education 0.0211 0.0505 0.42 0.6769
occupation 0.0309 0.0718 0.43 0.6666
rating -0.4685 0.0909 -5.15 2.6e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 675.38 on 600 degrees of freedom
Residual deviance: 609.51 on 592 degrees of freedom
AIC: 627.5
Number of Fisher Scoring iterations: 4
</code></pre>
<pre><code class="r">fit.reduced <- glm(ynaffair ~ age + yearsmarried + religiousness +
rating, data=Affairs, family=binomial())
summary(fit.reduced)
</code></pre>
<pre><code>
Call:
glm(formula = ynaffair ~ age + yearsmarried + religiousness +
rating, family = binomial(), data = Affairs)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.628 -0.755 -0.570 -0.262 2.400
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.9308 0.6103 3.16 0.00156 **
age -0.0353 0.0174 -2.03 0.04213 *
yearsmarried 0.1006 0.0292 3.44 0.00057 ***
religiousness -0.3290 0.0895 -3.68 0.00023 ***
rating -0.4614 0.0888 -5.19 2.1e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 675.38 on 600 degrees of freedom
Residual deviance: 615.36 on 596 degrees of freedom
AIC: 625.4
Number of Fisher Scoring iterations: 4
</code></pre>
<pre><code class="r">anova(fit.reduced, fit.full, test="Chisq")
</code></pre>
<pre><code>Analysis of Deviance Table
Model 1: ynaffair ~ age + yearsmarried + religiousness + rating
Model 2: ynaffair ~ gender + age + yearsmarried + children + religiousness +
education + occupation + rating
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 596 615
2 592 610 4 5.85 0.21
</code></pre>
<pre><code class="r">coef(fit.reduced)
</code></pre>
<pre><code> (Intercept) age yearsmarried religiousness rating
1.93083 -0.03527 0.10062 -0.32902 -0.46136
</code></pre>
<pre><code class="r">exp(coef(fit.reduced))
</code></pre>
<pre><code> (Intercept) age yearsmarried religiousness rating
6.8952 0.9653 1.1059 0.7196 0.6304
</code></pre>
<pre><code class="r">exp(confint(fit.reduced))
</code></pre>
<pre><code>Waiting for profiling to be done...
</code></pre>
<pre><code> 2.5 % 97.5 %
(Intercept) 2.1256 23.3506
age 0.9323 0.9981
yearsmarried 1.0449 1.1718
religiousness 0.6027 0.8563
rating 0.5287 0.7493
</code></pre>
<pre><code class="r">
## Test the predictive effect of marriage rating and age
testdata <- data.frame(rating=c(1, 2, 3, 4, 5), age=mean(Affairs$age), yearsmarried=mean(Affairs$yearsmarried),religiousness=mean(Affairs$religiousness))
testdata$prob <- predict(fit.reduced, newdata=testdata, type="response")
testdata <- data.frame(rating=mean(Affairs$rating),
age=seq(17, 57, 10),
yearsmarried=mean(Affairs$yearsmarried),
religiousness=mean(Affairs$religiousness))
testdata$prob <- predict(fit.reduced, newdata=testdata, type="response")
## Test for overdispersion
fit <- glm(ynaffair ~ age + yearsmarried + religiousness +
rating, family = binomial(), data = Affairs)
fit.od <- glm(ynaffair ~ age + yearsmarried + religiousness +
rating, family = quasibinomial(), data = Affairs)
pchisq(summary(fit.od)$dispersion * fit$df.residual,fit$df.residual, lower = F)
</code></pre>
<pre><code>[1] 0.3401
</code></pre>
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