CNVtools-package |
CNVtools : CNV association studies |
A112 |
Copy Number Variant intensity data |
apply.ldf |
Applies a canonical correlation transformation to the data |
apply.pca |
Applies to the data a principal component analysis |
CNV.fitModel |
Fits a mixture of Gaussian to a set of one dimensional points. |
cnv.plot |
Plots posterior probabilty distributions |
CNVtest.binary |
Fits a mixture of Gaussian to CNV data |
CNVtest.binary.T |
CNV association testing using T distributions |
CNVtest.qt |
Fits a mixture of Gaussian to CNV data |
CNVtest.qt.T |
Fits a mixture of Gaussian to CNV data |
CNVtest.select.model |
Select number of components in a CNV |
CNVtools |
CNVtools : CNV association studies |
compact.data.frame |
Compacts the expanded data frame format needed by our fitting procedure into more compact and user friendly version |
EM.starting.point |
Randomly assigns a starting point for the EM algorithm |
ExpandData |
Expands a CNV input data frame for the maximum likelihood routines |
get.model.spec |
Get model specifications (internal function) |
getparams |
Return mixture parameters |
getQualityScore |
Computes a quality score for a CNV fit |
qt.plot |
Makes signal vs trait plots and posterior probabilty distributions |
test.posterior |
Checks posterior probabilities are monotonic. |