Nothing

```
# This method implements the Least Slope Estimator proposed by
# Benjamini-Hochberg (2000) and described by Xu, Zhou, and Zhao
# in their Group Benjamini-Hochberg paper.
#
# 1) For p-values ingroup g, starting from i = 1, compute
# \code{l\_{g},i = (n\_{g} + 1 - i)/(1 - P\_{g,i})} where n\_g is the number
# of p-values in group g and P_g,i is the ith ordered p-value
# in group g.
# 2) As i increases, stop when \code{l\_{g},j > l\_{g,(j - 1)}} for the first
# time.
# 3) For each group, compute the LSL estimator of pi_{g,0} as
# \code{gamma\_{g, lsl} = min((floor(l\_{g},j) + 1)/n\_{g, 1}).
#
# Here are details of this function.
# Input: 1) p.val [numeric vector] - A vector of p-values.
# Output: 1) pi0 - An estimate for the proportion of null
# hypotheses within the set of p-values provided.
pi0.lsl <- function(p.val){
p.val <- sort(p.val)
n_g <- length(p.val)
i <- 1
while(TRUE){
if(i >= 2){
l_g.i.prev <- l_g.i
} else {
l_g.i.prev <- 10000 # We don't want to stop on the first iteration, so
# we set the values used to estimate pi0 very high
}
if(p.val[i] < 1){
l_g.i <- (n_g + 1 - i)/(1 - p.val[i])
} else {
return(l_g.i.prev) # If you're dividing by zero, then you guarantee an increase
}
if(l_g.i > l_g.i.prev || i == length(p.val)){
pi0 <- (floor(l_g.i) + 1)/n_g
pi0 <- min(pi0, 1)
return(pi0)
}
i <- i + 1
}
}
```

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