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`

”.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
#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
``` |

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