| Type: | Package |
| Title: | Robust Methods using Exponential Tilt Model |
| Version: | 1.0 |
| Date: | 2016-3-27 |
| Author: | Chuan Hong, Yong Chen, Yang Ning, Hao Wu |
| Maintainer: | Chuan Hong <hong.chuan.hannah@gmail.com> |
| Imports: | stats |
| Description: | Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison. |
| Depends: | R (≥ 2.5.0) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| LazyLoad: | no |
| NeedsCompilation: | yes |
| Packaged: | 2016-03-29 17:47:46 UTC; chong |
| Repository: | CRAN |
| Date/Publication: | 2016-03-29 20:04:40 |
Robust Exponential Tilt Mixture Model
Description
The package robustETM consists of the functions to perform pseudolikelihood based EM test for homogeneity in generalized exponential tilt mixture models.
Details
| Package: | robustETM |
| Type: | robustETM |
| Version: | 1.0 |
| Date: | 2016-03-27 |
| License: GPL>=2 | |
Testing for homogeneity in generalized exponential titl mixture model
Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely the generalized exponential tilt mixture model,
to account for heterogeneity between differentially methylated and non-differentially methylated subjects in the cancer group, and capture the
differences in higher order moments (e.g. mean and variance) between subjects
in cancer and normal groups. A pairwise pseudolikelihood is constructed
to eliminate the unknown nuisance function. To circumvent boundary and
non-identifiability problems as in parametric mixture models, we modify the
pseudolikelihood by adding a penalty function. In addition, test with simple
asymptotic distribution has computational advantages over permutational
test for high-dimensional genetic and epigenetic data. We propose a pseudolikelihood
based expectation-maximization test, and show the proposed test
follows a simple chi-squared limiting distribution.
The methods contains in function sim are:
-
The proposed PLEMT test (pseudolikelihood based EM test)
-
The t-test
-
The modified empirical likelihood ratio test
-
The empirical likelihood ratio test
-
The logistic regression test
-
The Wilcoxon test
-
The F test
-
The KS test
Author(s)
Chuan Hong
Yong Chen
Yang Ning
Hao Wu
Maintainer: Chuan Hong <hong.chuan.hannah@gmail.com>
References
Hong, C., Chen Y., Ning Y., Wang S., Wu H. and Carroll R.J. (2016). PLEMT: A novel pseudolikelihood based EM test for homogeneity in generalized exponential tilt mixture model (in preparation).
Tests under comparision for testing for homogeneity in generalized exponential tilt mixture models
Description
The function conducts the pseudolikelihood based EM test for homogeneity in generalized exponential tilt mixture models
Usage
sim(itr, K, cc, i.n, isetting, lambda, distn)
Arguments
itr |
random seed |
K |
Number of grid values for proportion parameter lambda |
cc |
Tuning parameter C for penalty function |
isetting |
Type I error or power scenarios I II and III for simulation study |
lambda |
Proportion parameter lambda |
i.n |
Sample size setting |
distn |
Distribution |
Value
mplrt_EM.TS |
Test statistic for the proposed PLEMT test |
qin.TS |
Test statistic for empirical likelihood ratio test |
liu.TS |
Test statistic for modified empirical likelihood ratio test |
t.TS |
Test statistic for t-test |
wilcox.p |
p-value for wilcoxon test |
logist.TS |
Test statistic for logistic regression test |
Author(s)
Chuan Hong, Yong Chen, Yang Ning, Hao Wu
References
Hong, C., Chen Y., Ning Y., Wang S., Wu H. and Carroll R.J. (2016). PLEMT: A novel pseudolikelihood based EM test for homogeneity in generalized exponential tilt mixture model (in preparation).
Examples
# not run
#myresult=sim(itr=1234, K=10, cc=20, i.n=2, isetting=1, lambda=0.3, distn="norm")