> chisq.test(matrix(c(279, 73, 225, 165, 47, 191), ncol = 3, byrow = T))

         Pearson's chi-square test without Yates' continuity correction 

data:  matrix(c(279, 73, 225, 165, 47, 191), ncol = 3, byrow = T) 
X-squared = 7.0095, df = 2, p-value = 0.0301 

> data <- read.table("gendergap.dat", header= T)  # read separate file
> data
  gender party count 
1 female     d   279
2 female     i    73
3 female     r   225
4   male     d   165
5   male     i    47
6   male     r   191
> attach(data)

> gendergap.fit <- glm(count ~ gender + party, family = poisson(link=log), 
resid = "pearson")  # generalized lin. model for log count


> gendergap.fit
Call:
glm(formula = count ~ gender + party, family = poisson(link = log), resid = 
        "pearson")

Coefficients:
 (Intercept)     gender     party1    party2 
    4.928837 -0.1794529 -0.6541664 0.1963424

Degrees of Freedom: 6 Total; 2 Residual
Residual Deviance: 7.002594 

> 1 - pchisq(7.0026, 2)  # P-value
[1] 0.03015815

> expected <- fitted(gendergap.fit)  # estimated expected frequencies

> residual <- (count - expected)/sqrt(expected)  #  Pearson residuals

> count; expected; residual
   1  2   3   4  5   6 
 279 73 225 165 47 191
>         1        2        3        4        5        6 
 261.4163 70.65306 244.9306 182.5837 49.34694 171.0694
>         1         2       3         4          5        6 
 1.087535 0.2792134 -1.2735 -1.301304 -0.3340963 1.523823

> matrix(c(count,expected,residual),ncol=3)
     [,1]      [,2]       [,3] 
[1,]  279 261.41633  1.0875350
[2,]   73  70.65306  0.2792134
[3,]  225 244.93061 -1.2735005
[4,]  165 182.58367 -1.3013036
[5,]   47  49.34694 -0.3340963
[6,]  191 171.06939  1.5238229


> count2 <- c(279,73,165,47)  # Next show decomposition of G-squared
> party2 <- scan(,"")
1: d
2: i
3: d
4: i
5: 
> gender2 <- scan(,"")
1: f
2: f
3: m
4: m
5: 
> fit2 <- glm(count2 ~ party2+gender2, family=poisson(link=log))
> fit2

Degrees of Freedom: 4 Total; 1 Residual
Residual Deviance: 0.1611715 

> count3 <- c(352, 225, 212, 191)
> fit3 <- glm(count3 ~ party2+gender2, family=poisson(link=log))
> fit3

Degrees of Freedom: 4 Total; 1 Residual
Residual Deviance: 6.841422