HyperSelect {BVSNLP} | R Documentation |

## Hyperparameter selection for iMOM prior density

### Description

This function finds data specific hyperparameters for inverse
moment prior density so that the overlap between the iMOM prior and null
MLE density is `1/\sqrt p`

. In this algorithm `r`

is always chosen
to be equal to 1 and `tau`

is found based on the mentioned overlap.

### Usage

```
HyperSelect(
X,
resp,
eff_size = 0.7,
nlptype = "piMOM",
iter = 10000,
mod_prior = c("unif", "beta"),
family = c("logistic", "survival")
)
```

### Arguments

`X` |
The design matrix. |

`resp` |
For logistic regression models, it is the binary response vector. For Cox proportional hazard model, this is a two column matrix where the first column contains survival time vector and the second column is the censoring status for each observation. |

`eff_size` |
This is the expected effect size in the model for a standardized design matrix, which is basically the coefficient value that is expected to occur the most based on some prior knowledge. |

`nlptype` |
Determines the type of nonlocal prior that is used in the analyses. It can be "piMOM" for product inverse moment prior, or "pMOM" for product moment prior. The default is set to piMOM prior. |

`iter` |
The number of iteration needed to simulate from null model in order to approximate the null MLE density. |

`mod_prior` |
Type of prior used for model space. |

`family` |
Determines the type of data analysis. |

### Value

It returns a list having following object:

`tau` |
The hyperparameter for iMOM prior density function, calculated using the proposed algorithm for the given dataset. |

### Author(s)

Amir Nikooienejad

### References

Nikooienejad, A., Wang, W., and Johnson, V. E. (2016). Bayesian
variable selection for binary outcomes in high dimensional genomic studies
using nonlocal priors. Bioinformatics, 32(9), 1338-1345.

Johnson, V. E., and Rossell, D. (2010). On the use of nonlocal prior
densities in Bayesian hypothesis tests. Journal of the Royal Statistical
Society: Series B (Statistical Methodology), 72(2), 143-170.

### Examples

```
### Simulating Logistic Regression Data
n <- 20
p <- 100
set.seed(321)
Sigma <- diag(p)
full <- matrix(c(rep(0.5, p * p)), ncol = p)
Sigma <- full + 0.5 * Sigma
cholS <- chol(Sigma)
Beta <- c(1, 1.8, 2.5)
X = matrix(rnorm(n*p, 1, 2), ncol = p)
X = X%*%cholS
beta <- numeric(p)
beta[c(1:length(Beta))] <- Beta
XB <- X%*%beta
probs <- as.vector(exp(XB)/(1+exp(XB)))
y <- rbinom(n,1,probs)
colnames(X) <- paste("gene_",c(1:p),sep="")
Xout <- PreProcess(X)
XX <- Xout$X
hparam <- HyperSelect(XX, y, iter = 1000, mod_prior = "beta",
family = "logistic")
hparam$tau
```

*BVSNLP*version 1.1.9 Index]