The model parameters z and W

Description

Contribution of each sample to a dependency model, and contribution of each variable.

Usage

z.effects(model, X, Y = NULL)
W.effects(model, X, Y = NULL)

Arguments

Details

z.effects gives the contribution of each sample to the dependency score. This is approximated by projecting original data to first principal component of Wz. This is possible only when the data window is smaller than half the number of samples.

W.effects gives the contribution of each variable to the observed dependency. This is approximated with the loadings of the first principal component of Wz

Original data can be retrieved by locating the row in X (or Y) which has the same variable (gene) name than model.

Value

z.effects gives a projection vector over the samples and W.effects gives a projection vector over the variables.

Author(s)

Olli-Pekka Huovilainen ohuovila@gmail.com and Leo Lahti leo.lahti@iki.fi

References

Dependency Detection with Similarity Constraints, Lahti et al., 2009 Proc. MLSP'09 IEEE International Workshop on Machine Learning for Signal Processing, See http://www.cis.hut.fi/lmlahti/publications/mlsp09_preprint.pdf

A Probabilistic Interpretation of Canonical Correlation Analysis, Bach Francis R. and Jordan Michael I. 2005 Technical Report 688. Department of Statistics, University of California, Berkley. http://www.di.ens.fr/~fbach/probacca.pdf

Probabilistic Principal Component Analysis, Tipping Michael E. and Bishop Christopher M. 1999. Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611–622. http://research.microsoft.com/en-us/um/people/cmbishop/downloads/Bishop-PPCA-JRSS.pdf

See Also

DependencyModel-class, screen.cgh.mrna

Examples

data(chromosome17)
window <- fixed.window(geneExp, geneCopyNum, 150, 10)

## pSimCCA model around one gene
depmodel <- fit.dependency.model(window$X, window$Y)
# Conversion from DependencyModel to GeneDependencyModel so that gene name and location can be stored
depmodel <- as(depmodel,"GeneDependencyModel")
setGeneName(depmodel) <- window$geneName
setLoc(depmodel) <- window$loc
barplot(z.effects(depmodel, geneExp, geneCopyNum))

## Plot the contribution of each genes to the model. Only the X component is plotted
## here since Wx = Wy (in SimCCA) 
barplot(W.effects(depmodel, geneExp, geneCopyNum)$X)

## plot.DpenendencyModel shows also sample and variable effects
plot(depmodel,geneExp,geneCopyNum)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(pint)
Loading required package: mvtnorm
Loading required package: Matrix
Loading required package: dmt
Loading required package: MASS

dmt Copyright (C) 2008-2013 Leo Lahti and Olli-Pekka Huovilainen.
This program comes with ABSOLUTELY NO
WARRANTY.
This is free software, and you are welcome to redistribute it
under the FreeBSD license.



pint Copyright (C) 2008-2013 Olli-Pekka Huovilainen and Leo Lahti.

This program comes with ABSOLUTELY NO WARRANTY.
This is free software, and you are welcome to redistribute it
under the FreeBSD license.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/pint/z.effect.Rd_%03d_medium.png", width=480, height=480)
> ### Name: z.effects
> ### Title: The model parameters z and W
> ### Aliases: z.effects W.effects
> ### Keywords: math
> 
> ### ** Examples
> 
> data(chromosome17)
> window <- fixed.window(geneExp, geneCopyNum, 150, 10)
> 
> ## pSimCCA model around one gene
> depmodel <- fit.dependency.model(window$X, window$Y)
Matched variables but priors$Nm.wxwy.sigma not given, using strong matching with Wx = Wy.
The matrix Nm.wxwy.mean is not specified. Using identity matrix.
> # Conversion from DependencyModel to GeneDependencyModel so that gene name and location can be stored
> depmodel <- as(depmodel,"GeneDependencyModel")
> setGeneName(depmodel) <- window$geneName
> setLoc(depmodel) <- window$loc
> barplot(z.effects(depmodel, geneExp, geneCopyNum))
> 
> ## Plot the contribution of each genes to the model. Only the X component is plotted
> ## here since Wx = Wy (in SimCCA) 
> barplot(W.effects(depmodel, geneExp, geneCopyNum)$X)
> 
> ## plot.DpenendencyModel shows also sample and variable effects
> plot(depmodel,geneExp,geneCopyNum)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>