sigaR (version 1.18.0)

RCMestimation: Fitting of the random coefficients model.

Description

The parameters of the random coefficients model are estimated by means of the maximum likelihood method. The implemented maximum likelihood procedure has been optimized with respect to computational efficiency and memory usage.

Usage

RCMestimation(Y, X, R, hypothesis = "H2", shrinkType = "none", 
estType = "normal", corType = "unif", maxNoIt = 100, 
minSuccDist = 0.005, verbose = FALSE)

Arguments

Y

The matrix containing the (e.g., expression) data (number of columns equal to number of features, number of rows equal to number of samples).

X

The design matrix (number of rows equal to number of samples, number of columns equal to number of covariates).

R

The linear constraint matrix (number of columns equal to the number of covariates).

hypothesis

The hypothesis under which the model is fitted: H0 (H0 : R beta = 0 & tau2 = 0), H1 (H1 : R beta >= 0 & tau2 = 0), H2 (H2 : R beta >= 0 & tau2 >= 0).

shrinkType

The type of shrinkage to be applied to the error variances: none (shrinkage parameter is set equal to zero: no shrinkage), opt (shrinkage parameter is chosen to minimize the mean squared error criterion) or full (shrinkage parameter is set equal to one).

estType

Type of estimation, either normal (non-robust) or robust.

corType

Correlation structure to be used, either unif or ar1.

maxNoIt

Maximum number of iterations in the ML procedure.

minSuccDist

Minimum distance between estimates of two successive iterations to be achieved.

verbose

Logical indicator: should intermediate output be printed on the screen?

Value

Object of class rcmFit.

Note

In case a covariate for the intercept is included in the design matrix X we strongly recommend the center, per feature, the data around zero.

Details

Details on the type of random coefficients model that is actually fitted are specified in the reference below.

References

Van Wieringen, W.N., Berkhof, J., Van de Wiel, M.A. (2010), "A random coefficients model for regional co-expression associated with DNA copy number", Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue1, Article 25, 1-28.

See Also

RCMrandom, RCMtest, rcmTest.

Examples

# load data
data(pollackCN16)
data(pollackGE16)

# select features belonging to a region
ids <- getSegFeatures(20, pollackCN16)

# extract segmented log2 ratios of the region
X <- t(segmented(pollackCN16)[ids[1], , drop=FALSE])

# extract segmented log2 ratios of the region
Y <- exprs(pollackGE16)[ids,]

# center the expression data (row-wise)
Y <- t(Y - apply(Y, 1, mean))

# specify the linear constraint matrix
R <- matrix(1, nrow=1)

# fit the random coefficients model to the random data
RCMresults <- RCMestimation(Y, X, R)