Purposeless Life

R easily computes Chi-square tests for any supplied matrix.
Reminder: R and matrices
The basic R command for a matrix is to start with a list of elements(c(,,,)) and the number of rows nrow in the matrix.
> args(matrix)
function (data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL)

For example, if we have the 2x4 contingency table
9   15   5   15
4   12   10  4
We express this as a matrix with the command
> M<-matrix(c(9,4,15,12,5,10,15,4), nrow=2, byrow=F)
It's always a good idea to check to see that you have correctly enter the matrix. Typing
> M

To perform Pearson's Chi-squared test, simply put the contingency table data into a matrix.
> chisq.test(M)
For the above table, R returns:
        Pearson's Chi-squared test
data:  M
X-squared = 7.9266, df = 3, p-value = 0.04755

Note, however, that some of the cells of the table have values of 5 or less. In such cases, Fisher's exact test is better.
> fisher.test(M)
        Fisher's Exact Test for Count Data
data:  M
p-value = 0.05133
alternative hypothesis: two.sided

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批量计算：
x=read.table("C:\...)
d=nrow(x)
z<-matrix(0,d,d)
z
for(i in 1:d)
for(j in 1:d)
z[[i,j]]<-chisq.test(x[,i],x[,j])$p.value
round(z,3)
write.table(z,file="R_tmp")

List to Vector: unlist
M<-matrix(unlist(x[i,]),nrow=2,byrow=T)