> # Goodness of fit example (Sokal & Rohlf Box 17.1 Part 1 pp 699)
> pheno <- c(63,31,28,12,39,16,40,12)
> mendel <- c(18,6,6,2,12,4,12,4)
> expected.prob <- mendel/sum(mendel)
> g.test(pheno,p=expected.prob)

	Log likelihood ratio (G-test) goodness of fit test

data:  pheno 
Log likelihood ratio statistic (G) = 8.824, X-squared df = 7, p-value = 0.2655 

> g.test(pheno,p=expected.prob,correct="williams")

	Log likelihood ratio (G-test) goodness of fit test

data:  pheno 
Log likelihood ratio statistic (G) = 8.7694, X-squared df = 7, p-value = 0.2696 

> # 2cell Goodness of fit example (Sokal & Rohlf Box 17.1 Part 2 pp 700)
> pheno <- c(130,46)
> expected <- c(0.75,0.25)
> g.test(pheno,p=expected)

	Log likelihood ratio (G-test) goodness of fit test

data:  pheno 
Log likelihood ratio statistic (G) = 0.12, X-squared df = 1, p-value = 0.729 

> g.test(pheno,p=expected,correct="williams")

	Log likelihood ratio (G-test) goodness of fit test

data:  pheno 
Log likelihood ratio statistic (G) = 0.1197, X-squared df = 1, p-value = 0.7294 

> g.test(pheno,p=expected,correct="yates")

	Log likelihood ratio (G-test) goodness of fit test

data:  pheno 
Log likelihood ratio statistic (G) = 0.0677, X-squared df = 1, p-value = 0.7948 

> # 2x2 Test of independence example (Sokal & Rohlf Box 17.6 pp 731)
> serp <- c(12,22)
> non.serp <- c(16,50)
> data.mat <- rbind(serp,non.serp)
> data.mat
         [,1] [,2]
serp       12   22
non.serp   16   50
> g.test(data.mat)$expected
          [,1]  [,2]
serp      9.52 24.48
non.serp 18.48 47.52
> g.test(data.mat)

	Log likelihood ratio (G-test) test of independence without correction

data:  data.mat 
Log likelihood ratio statistic (G) = 1.3325, X-squared df = 1, p-value = 0.2484 

> g.test(data.mat,correct="williams")

	Log likelihood ratio (G-test) test of independence with Williams'
	correction

data:  data.mat 
Log likelihood ratio statistic (G) = 1.3028, X-squared df = 1, p-value = 0.2537 

> g.test(data.mat,correct="yates")

	Log likelihood ratio (G-test) test of independence with Yates'
	correction

data:  data.mat 
Log likelihood ratio statistic (G) = 0.8521, X-squared df = 1, p-value = 0.356 

> # RxC Test of independence example (Sokal & Rohlf Box 17.8 pp 738)
> red <- c(29,273,8,64)
> not.red <- c(11,191,31,64)
> data.mat <- cbind(red,not.red)
> data.mat
     red not.red
[1,]  29      11
[2,] 273     191
[3,]   8      31
[4,]  64      64
> g.test(data.mat)

	Log likelihood ratio (G-test) test of independence without correction

data:  data.mat 
Log likelihood ratio statistic (G) = 28.5964, X-squared df = 3, p-value = 2.722e-06 

> g.test(data.mat,correct="williams")

	Log likelihood ratio (G-test) test of independence with Williams'
	correction

data:  data.mat 
Log likelihood ratio statistic (G) = 28.3125, X-squared df = 3, p-value = 3.123e-06 

>