plotCovar | R Documentation |
Plot an the corvariance matrix of the test statistics estimated through permutations
plotCovar(
reconsiFit,
col = colorRampPalette(c("yellow", "blue"))(12),
xlab = "Test statistic index",
ylab = xlab,
...
)
reconsiFit |
The reconsi fit |
col, xlab, ylab, ... |
A list of arguments for the image() function. |
By default, yellow indicates negative correlaton between test statistics, blue positive correlation
invisible()
Note the difference with the plotApproxCovar function, where the covariances between binned test statistics are shown to get an idea between covariances between tail and center values of the univariate null distribution. Here the covariance matrix between all test statistics is shown
plotApproxCovar
p = 200; n = 50; B = 5e1
x = rep(c(0,1), each = n/2)
mat = cbind(
matrix(rnorm(n*p/10, mean = 5+x),n,p/10), #DA
matrix(rnorm(n*p*9/10, mean = 5),n,p*9/10) #Non DA
)
mat = mat = mat + rnorm(n, sd = 0.3) #Introduce some dependence
fdrRes = reconsi(mat, x, B = B)
plotCovar(fdrRes)
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