confSAM {confSAM} | R Documentation |
Computes a confidence upper bound for the False Discovery Proportion (FDP). The input required is a matrix containing test statistics (or p-values) for (randomly) permuted versions of the data.
confSAM(p, PM, includes.id=TRUE, cutoff=0.01, reject="small", alpha=0.05,
method="simple", ncombs=1000)
p |
A vector containing the p-values for the original (unpermuted) data. |
PM |
A matrix (with |
includes.id |
Set this to |
cutoff |
A number or a vector of length |
reject |
If |
alpha |
1-alpha is the desired confidence level of the bounds. |
method |
If |
ncombs |
Only applies when |
A vector with three values is returned. The first value is the number if rejections. The second value is a basic median unbiased estimate of the number of false positives V. This estimate coincides with the simple upper bound for alpha=0.5. The third value is a (1-alpha)-confidence upper bound for V (it depends on the argument method
which bound this is.)
#This is a fast example. It is recommended to take w and ncombs larger in practice.
set.seed(423)
m <- 100 #number of hypotheses
n <- 10 #the amount of subjects is 2n (n cases, n controls).
w <- 50 #number of random permutations. Here we take w small for computational speed
X <- matrix(rnorm((2*n)*m), 2*n, m)
X[1:n,1:50] <- X[1:n,1:50]+1.5 # the first 50 hypotheses are false
#(increased mean for the first n individuals).
y <- c(numeric(n)+1,numeric(n)-1)
Y <- t(replicate(w, sample(y, size=2*n, replace=FALSE)))
Y[1,] <- y #add identity permutation
pvalues <- matrix(nrow=w,ncol=m)
for(j in 1:w){
for(i in 1:m){
pvalues[j,i] <- t.test( X[Y[j,]==1,i], X[Y[j,]==-1,i] ,
alternative="two.sided" )$p.value
}
}
## number of rejections:
confSAM(p=pvalues[1,], PM=pvalues, cutoff=0.05, alpha=0.1, method="simple")[1]
## basic median unbiased estimate of #false positives:
confSAM(p=pvalues[1,], PM=pvalues, cutoff=0.05, alpha=0.1, method="simple")[2]
## basic (1-alpha)-upper bound for #false positives:
confSAM(p=pvalues[1,], PM=pvalues, cutoff=0.05, alpha=0.1, method="simple")[3]
## potentially smaller (1-alpha)-upper bound for #false positives:
## (taking 'ncombs' much larger recommended)
confSAM(p=pvalues[1,], PM=pvalues, cutoff=0.05, alpha=0.1, method="approx",
ncombs=50)[3]
## actual number of false positives:
sum(pvalues[1,51:100]<0.05)