caution.parameter.actions: Based on a Decision-Theoretic Approach, Performs a Multiple...
In LFDREmpiricalBayes: Estimating Local False Discovery Rates Using Empirical Bayes Methods

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Assuming a zero-onr loss function, it provides three caution-type actions using estimated LFDRs computed based on both separate and combined reference classes.

1 2	caution.parameter.actions(x1,x2,l1=4,l2=1) # default values l1=4 and l2=1 # to obtain a threshold of 20%.

`x1`	A vector of LFDRs in the combined reference class.
`x2`	A vector of LFDRs in the separate reference class.
`l1`	Loss value (Type-I error) for deriving the threshold of the Bayes action.
`l2`	Loss value (Type-II error) for deriving the threshold of the Bayes action.

Accepts previously obtained LFDR estimates of SNPs falling inside the intersection of the separate and combined reference classes. The LFDR estimates of some biological feature (SNP or gene) within a sample population that we will refer to as ‘reference class’. If a reference class, containing LFDR estimates is a subset of the other, it is referred to as ‘separate class’. The entire set of LFDR estimates is called a ‘combined’ reference class. Then, a multiple hypothesis problem is conducted using three caution-type estimators. The threshold set for rejecting the null hypothesis is derived from pre-specified l1 and l2 values. Since having a type-I error is worse than a type-II error, l1 is recommende to be greater than l2.

In generating the output, there are two potential outputs for each index of the three caution-type actions. Check the Value section for the corresponding caution-type actions.

For each index of the output, one of two potential outputs based on Bayes action are shown:

`0`	Do not reject the null hypothesis
`1`	Reject the null hypothesis

For each corresponding index in the output, the decision on whether to reject or not reject the null hypothesis for biological feature can be based on CGM1, CGM0, and CGM0.5 decisions. Check See Also for more details on how to better interpret the outputs.

Outputs three vectors of equal size as seen below:

`CGM1`	Decision values for the Conditional Gamma Minimax (CGMinimax).
`CGM0`	Decision values for the Conditional Gamma Minimin (CGMinimin).
`CGM0.5`	Decision values for the CG0.5 caution case (a balance between CGMinimax and CGMinimin.

Note that the length of the input vectors x1 and x2 determines the number of null hypothesis values seen in the output.

A limitation to the code is that both reference classes: x1 and x2 must be of the same vector length.

Code: Ali Karimnezhad.
Documentation: Justin Chitpin, Anna Akpawu and Johnary Kim.

Karimnezhad, A. and Bickel, D. R. (2016). Incorporating prior knowledge about genetic variants into the analysis of genetic association data: An empirical Bayes approach. Working paper. Retrieved from http://hdl.handle.net/10393/34889

For more information on how to interpret the outputs, look at the vignette, “Using LFDREmpiricalBayes”.

#LFDR reference class values generated

#First reference class (separate class)
LFDR.Separate <- c(.14,.8,.251,.30)
#Second reference class (combined class)
LFDR.Combined <- c(.21,.61,.0888,.10)

# Default threshold at (20%).
output <- caution.parameter.actions(x1=LFDR.Separate, x2=LFDR.Combined)

# Three caution cases
output