How get matrix of p-values for ks test and t test?

Question

I am new with R. If you could help me that would be great. My problem is as follows:

Lets say I have 5 groups, Group1, Group2, Group3, Group4 and Group5, each containing 100 data points.

Now I want to compare these groups with each other, using either t-test or ks-test and want to generate a matrix of p-values. Essentially, there would a 5x5 matrix of p-values. I have done similar kind of work with correletions using corr.mat function. Here, 5 groups are for just illustrative purpose, at the end of the day I ahve to do it on almost 250 groups thus I have to generate a matrix of 250x250 containing p-values.

If anyone of you could help me to achieve this, it would be much kind of you.

Things I know in R so far:

Load the data into R by loading .csv file:

my.data = read.csv(file.choose())
attach(your.data)

Answer 1

If you know how to compute an individual p-value, you can just put that code in a loop.

# Sample data
d <- data.frame(
  group = paste( "group", rep(1:5, each=100) ),
  value = rnorm( 5*100 )
)

# Matrix to store the result
groups <- unique( d$group )
result <- matrix(NA, nc=length(groups), nr=length(groups))
colnames(result) <- rownames(result) <- groups

# Loop
for( g1 in groups ) {
  for( g2 in groups ) {
    result[ g1, g2 ] <- t.test( 
      d$value[ d$group == g1 ], 
      d$value[ d$group == g2 ]
    )$p.value              
  }
}
result

#           group 1   group 2   group 3   group 4   group 5
# group 1 1.0000000 0.6533393 0.7531349 0.6239723 0.6194475
# group 2 0.6533393 1.0000000 0.9047020 0.9985489 0.3316215
# group 3 0.7531349 0.9047020 1.0000000 0.8957871 0.4190027
# group 4 0.6239723 0.9985489 0.8957871 1.0000000 0.2833226
# group 5 0.6194475 0.3316215 0.4190027 0.2833226 1.0000000

You could also use outer:

groups <- unique( d$group )
outer( 
  groups, groups, 
  Vectorize( function(g1,g2) {
    t.test( 
      d$value[ d$group == g1 ], 
      d$value[ d$group == g2 ]
    )$p.value
  } )
)