Hi there!! I am trying to utilize the pooled.Chi routine found in this paper "https://www.niehs.nih.gov/research/resources/software/biostatistics/scd/index.cfm" what I am trying to is to adjust a set of P-values from one phenotype using a set of values from a second phenotype, "that is what this routine is supposed to do". I am trying to adjust a total of 100 P-values in a loop so that P-value 1, would adjust P-value 2 and it would output and adjusted P-value, then move to the next pair of P-values. However, when the function Fareborother is called, it returns the same value per each pair from 1:100.

Has anyone used this package before? and if so, what should I do to get the correct output?

Below is how I call the pooled.Chi routine.

```
n <- c(500, 99, 99)
# vector of p-values
dat1 <- fread("psp_ad_onehundred_rown.txt")
p1 <- dat1$PSP_P
p2 <- dat1$AD_P
for (i in 3:99){
p <- c(p1[i], p2[i])
# combined p-value for the Z method
#pz <- pooled.Z(n, p, effect.signs)
#pza <- append(pza, pz)
# combined p-value for the chi-square method
pchi <- pooled.Chi(n, p)
# adjust the first p-value (0.07) given the second one (0.04)
#pcond <- conditional.Z(n[1:3], p[1:2], effect.signs[1:2])
cat(pchi)
}
```

Thank you

Without looking into the details of this, I think you need to pass the whole series of your 100 paired p-values to the function. This is because I suspect that their distributions play a role. I don't see how having two p-values without context would allow one to be used to correct the other.