## Probit transform of p-values
## from a Beta-Uniform mixture model
## comparion of parametric and semiparametric EM fit
## Note: in actual situations n=thousands
set.seed(50)
n=300 # nb of multiple tests
m=2 # 2 mixture components
a=c(1,0.1); b=c(1,1); lambda=c(0.6,0.4) # parameters
z=sample(1:m, n, rep=TRUE, prob = lambda)
p <- rbeta(n, shape1 = a[z], shape2 = b[z]) # p-values
o <- order(p)
cpd <- cbind(z,p)[o,] # sorted complete data, z=1 if H0, 2 if H1
p <- cpd[,2] # sorted p-values
y <- qnorm(p) # probit transform of the pvalues
# gaussian EM fit with component 1 constrained to N(0,1)
s1 <- normalmixEM(y, mu=c(0,-4),
mean.constr = c(0,NA), sd.constr = c(1,NA))
s2 <- spEMsymlocN01(y, mu0 = c(0,-3)) # spEM with N(0,1) fit
plotly_spEMN01(s2 , add.plot = FALSE)
Run the code above in your browser using DataLab