Dear R Programmers,
I am trying to run a Poisson regression on all pairs of variables in a data
set and
obtain the permutation distribution. The number of pairs is around 100000.
It seems my code will take weeks to run, unless I try something else.
Could you give me any suggestions on how to improve the speed of the
code below, or any general suggestions on how I may accomplish this task.
Thanks for your time,
Juliet

To run this code, first enter the model matrices (matrices given at bottom):

# X_red <- as.matrix(read.table("clipboard",header=F),nrow=18,byrow=T)
# X_full <- as.matrix(read.table("clipboard",header=F),nrow=18,byrow=T)


library(combinat)
# make some example data; the actual data is 700x800
myData <- matrix(sample(c(1:3),500,replace=TRUE),nrow=100,ncol=5)
# the response is binary
response <- c(rep(1,50),rep(0,50))
# initalize permutation of response 'labels'.
perm.response <- response

counts <- rep(1,18)

# Number of permutations
nperm <- 5

# matrix of all pairs of indices
all.pairs <- combn2(1:ncol(myData))
# initalize results
pmatrix <- matrix(-1,nrow=nperm,ncol=nrow(all.pairs))

getLRTpval <- function(index)
{

  # A contingency table is formed from two columns of the data and the
response (3 way table) and made into a vector
  counts <- as.vector(table(myData[,index[1]],myData[,index[2]],
perm.response));

  # Add 1 to any count that = 0.
  counts[counts == 0] <- 1
  reduced_model <- glm.fit(X_red,counts,family=poisson(link="log"))
  full_model <-    glm.fit(X_full,counts,family=poisson(link="log"))
  pval <- pchisq(reduced_model$deviance - full_model$deviance,
reduced_model$df.residual - full_model$df.residual, lower.tail= FALSE)
}


for (perm in 1:nperm)
{
  # Permute the labels
  perm.response <- sample(response,100,replace=TRUE)
  pmatrix[perm,] <- apply(all.pairs, 1, getLRTpval)
}

#X_red
1 1 0 1 0 1 1 0 0 0
1 1 0 0 1 1 0 1 0 0
1 1 0 -1 -1 1 -1 -1 0 0
1 0 1 1 0 1 0 0 1 0
1 0 1 0 1 1 0 0 0 1
1 0 1 -1 -1 1 0 0 -1 -1
1 -1 -1 1 0 1 -1 0 -1 0
1 -1 -1 0 1 1 0 -1 0 -1
1 -1 -1 -1 -1 1 1 1 1 1
1 1 0 1 0 -1 1 0 0 0
1 1 0 0 1 -1 0 1 0 0
1 1 0 -1 -1 -1 -1 -1 0 0
1 0 1 1 0 -1 0 0 1 0
1 0 1 0 1 -1 0 0 0 1
1 0 1 -1 -1 -1 0 0 -1 -1
1 -1 -1 1 0 -1 -1 0 -1 0
1 -1 -1 0 1 -1 0 -1 0 -1
1 -1 -1 -1 -1 -1 1 1 1 1


# X_full

1  1  0  1  0  1  1  0  0  0  1  0  1  0
1  1  0   0  1  1  0  1  0  0  1  0  0  1
1  1  0      -1 -1  1 -1 -1  0  0  1  0 -1 -1
1  0  1  1  0  1  0  0  1  0  0  1  1  0
1  0  1  0  1  1  0  0  0  1  0  1  0  1
1  0  1 -1 -1  1  0  0 -1 -1  0  1 -1 -1
1 -1 -1  1  0  1 -1  0 -1  0 -1 -1  1  0
1 -1 -1  0  1  1  0 -1  0 -1 -1 -1  0  1
1 -1 -1 -1 -1  1  1  1  1  1 -1 -1 -1 -1
1  1  0  1  0 -1  1  0  0  0 -1  0 -1  0
1  1  0  0  1 -1  0  1  0  0 -1  0  0 -1
1  1  0 -1 -1 -1 -1 -1  0  0 -1  0  1  1
1  0  1  1  0 -1  0  0  1  0  0 -1 -1  0
1  0  1  0  1 -1  0  0  0  1  0 -1  0 -1
1  0  1 -1 -1 -1  0  0 -1 -1  0 -1  1  1
1 -1 -1  1  0 -1 -1  0 -1  0  1  1 -1  0
1 -1 -1  0  1 -1  0 -1  0 -1  1  1  0 -1
1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1

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