simnewsamples: Posterior predictive simulation
In gaga: GaGa hierarchical model for high-throughput data analysis

Description Usage Arguments Details Value Author(s) References See Also

Posterior and posterior predictive simulation for GaGa/MiGaGa and Normal-Normal models.

1	simnewsamples(fit, groupsnew, sel, x, groups)

`fit`	Either GaGa or MiGaGa fit (object of type `gagafit`, as returned by `fitGG`) or Normal-Normal fit (type `nnfit` returned by `fitNN`).
`groupsnew`	Vector indicating the group that each new sample should belong to. `length(groupsnew)` is the number of new samples that will be generated.
`sel`	Numeric vector with the indexes of the genes we want to draw new samples for (defaults to all genes). If a logical vector is indicated, it is converted to `(1:nrow(x))[sel]`. For the Normal-Normal model this argument is ignored.
`x`	`ExpressionSet`, `exprSet`, data frame or matrix containing the gene expression measurements used to fit the model.
`groups`	If `x` is of type `ExpressionSet` or `exprSet`, `groups` should be the name of the column in `pData(x)` with the groups that one wishes to compare. If `x` is a matrix or a data frame, `groups` should be a vector indicating to which group each column in x corresponds to.

For GaGa/MiGaGa models, the shape parameters are actually drawn from a gamma approximation to their posterior distribution. The function rcgamma implements this approximation.

In order to be consistent with the LNNGV model implemented in emfit (package EBarrays), for the Normal-Normal model the variance is drawn from an inverse gamma approximation to its marginal posterior (obtained by plugging in the group means, see EBarrays vignette for details).

Object of class 'ExpressionSet'. Expression values can be accessed via exprs(object) and the parameter values used to generate the expression values can be accessed via fData(object).

David Rossell

Rossell D. (2009) GaGa: a Parsimonious and Flexible Model for Differential Expression Analysis. Annals of Applied Statistics, 3, 1035-1051.

Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.

checkfit for posterior predictive plot, simGG for prior predictive simulation.

gaga documentation built on Nov. 8, 2020, 5:49 p.m.