Description Usage Format Details Examples

Numbers of faults found in each of 32 rolls of fabric produced in a particular factory. Also given is the length of the roll.

1 |

A data frame with 32 observations on the following 2 variables.

`length`

length of roll

`faults`

number of faults in roll

The book uses this for exercise 5. page 441

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data(fabric)
str(fabric)
names(fabric)
# Identity link:
with(fabric, plot(faults ~ length))
# log link:
with(fabric, plot(faults ~ length, log="y"))
# Fitting poisson regression models:
mod1 <- glm(faults ~ length-1, data=fabric, family=poisson)
OK <- require(MCMCpack)
if(OK) mod2 <- MCMCpoisson(faults ~ length-1, data=fabric, b0=0, B0=0.0001)
summary(mod1)
confint(mod1)
if(OK) summary(mod2)
# The exercise is to investigate overdispersion ...
``` |

```
'data.frame': 32 obs. of 2 variables:
$ length: int 551 651 832 375 715 868 271 630 491 372 ...
$ faults: int 6 4 17 9 14 8 5 7 7 7 ...
[1] "length" "faults"
Loading required package: MCMCpack
Loading required package: coda
Loading required package: MASS
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2017 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
Call:
glm(formula = faults ~ length - 1, family = poisson, data = fabric)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1289 -0.4759 0.3938 1.2869 4.3445
Coefficients:
Estimate Std. Error z value Pr(>|z|)
length 3.242e-03 8.352e-05 38.81 <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: 839.793 on 32 degrees of freedom
Residual deviance: 80.554 on 31 degrees of freedom
AIC: 205.85
Number of Fisher Scoring iterations: 4
Waiting for profiling to be done...
2.5 % 97.5 %
0.003074660 0.003402216
Iterations = 1001:11000
Thinning interval = 1
Number of chains = 1
Sample size per chain = 10000
1. Empirical mean and standard deviation for each variable,
plus standard error of the mean:
Mean SD Naive SE Time-series SE
0.003242 0.000000 0.000000 0.000000
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
0.003242 0.003242 0.003242 0.003242 0.003242
```

BayesDA documentation built on May 29, 2017, 9:08 a.m.

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