# fabric: Numbers of Faults Found in Each of 32 Rolls of Fabric In BayesDA: Functions and Datasets for the book "Bayesian Data Analysis"

## Description

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

## Usage

 `1` ```data(fabric) ```

## Format

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

`length`

length of roll

`faults`

number of faults in roll

## Details

The book uses this for exercise 5. page 441

## Examples

 ``` 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 ... ```

### Example output

```'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"
##
## 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.