Ordinal procedure controlling the FDR and the Generalized FWER
1 2 3 
p 
vector of pvalues 
ord 
Values on the basis of which the procedure select the hypotheses (following decreasing order). The vector have the same length of 
q 
average FDR level 
alpha 
global significance level 
k 
number of allowed errors in kFWE controls 
J 
number of allowed jumps befor stopping 
alpha.prime 
univariate alpha for single step Guo and Romano procedure 
GD 
Logic value. Should the correction for general dependence be applied? 
The function returns an object of class someMTP.object
.

a logical vector indicating whenever the related hypotesis have been rejected. 

the vector of pvalues used in the call 

The vector used to sort the pvalues (decrasing). 

"fdrOrd" or "kfweOrd" 

A logical value incating if the correction for General Dependence have been used or not. 

The level of controlled FDR. 

The level of controlled kFWER 

The significance level of individual tests 

Number of allowed Errors 

Number of allowed Jumps 
L. Finos and A. Farcomeni
L. Finos, A. Farcomeni (2011). kFWER Control without pvalue Adjustment, with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins. Biometrics.
A. Farcomeni, L. Finos (2013). FDR Control with PseudoGatekeeping Based on a Possibly Data Driven Order of the Hypotheses. Biometrics.
See also draw
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  set.seed(17)
x=matrix(rnorm(60),3,20)
x[,1:10]=x[,1:10]+2 ##variables 1:10 have tests under H1
ts=apply(x,2,function(x) t.test(x)$statistic)
ps=apply(x,2,function(x) t.test(x)$p.value) #compute pvalues
m2=apply(x^2,2,mean) #compute ordering criterion
pOrd < fdrOrd(ps,q=.05,ord=m2) #ordinal Procedure
pOrd
draw(pOrd)
sum(p.adjust(ps,method="BH")<=.05) #rejections with BH
kOrd < kfweOrd(ps,k=5,ord=m2)#ordinal procedure
kOrd
kOrdGD < kfweOrd(ps,k=5,ord=m2,GD=TRUE)#ord. proc. (any dependence)
kOrdGD

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