# HMVD-package: Group Association Test using a Hidden Markov Model In HMVD: Group Association Test using a Hidden Markov Model

## Description

HMVD performs an association test between a group of variables and the outcome. Posterior probabilities are provided for each variable indicating how likely each variable is associated with the outcome.

## Details

 Package: HMVD Type: Package Version: 1.0 Date: 2016-05-12 License: GPL-3

~~ An overview of how to use the package, including the most important functions ~~

## Author(s)

Maintainer: Yichen Cheng<[email protected]>

## References

Cheng, Y., Dai, J. and Kooperberg, C. (2015). Group association test using hidden Markov model. Biostatistics, in pres.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```############################################################################# #### compute the p-value and do parameter estimation for continuous outcome n = 4000; m = 20 X = matrix(rnorm(n*m),n) Y = rowMeans(X[,1:4])*.2 + rnorm(n) HMVD(Y,X)\$p.value #### approximate p-value HMVD(Y,X,nperm.max = 20)\$p.value.perm #### p-value based on permutations #### in practice we would use more permutations out = HMVD(Y,X,method='estimation') round(out\$prob,2) ###posterior probability out\$theta ### common effect size #### compute the p-value and do parameter estimation for binary outcome n = 4000; m = 20 X = matrix(rnorm(n*m),n) p = rowMeans(X[,1:4])*.4 Y = rbinom(n,1,p = exp(p)/(1+exp(p))) HMVD(Y,X,model.type='D')\$p.value #### approximate p-value HMVD(Y,X,nperm.max = 20)\$p.value.perm #### p-value based on permutations #### in practice we would use more permutations out = HMVD(Y,X,model.type='D',method='estimation') round(out\$prob,2) ###posterior probability out\$theta ### common effect size ```

HMVD documentation built on May 29, 2017, 8:35 p.m.