RCMtest {sigaR} | R Documentation |
Function that evaluates various hypothesis within the random coefficients model via bootstrap resampling.
RCMtest(Y, X, R, testType = "I", nBoot = 100, lowCiThres = 0.1, shrinkType = "none", estType = "normal", corType = "unif", maxNoIt = 100, minSuccDist = 0.005, returnNullDist = FALSE, ncpus=1, verbose = FALSE)
Y |
The |
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). |
testType |
The hypothesis to be tested: |
nBoot |
Number of bootstraps. |
lowCiThres |
A value between 0 and 1. Determines speed of efficient p-value calculation. If the probability of a p-value being below |
shrinkType |
The type of shrinkage to be applied to the error variances: |
estType |
Type of estimation, either |
corType |
Correlation structure to be used, either |
maxNoIt |
Maximum number of iterations in the ML procedure. |
minSuccDist |
Minimum distance between estimates of two successive iterations to be achieved. |
returnNullDist |
Logical indicator: should the null distribution be returned? |
ncpus |
Number of cpus used for the bootstrap. |
verbose |
Logical indicator: should intermediate output be printed on the screen? |
Details on the type of random coefficients model that is actually fitted are specified in the reference below.
Object of class rcmTest
.
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.
Wessel N. van Wieringen: w.vanwieringen@vumc.nl
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.
Van Wieringen, W.N., Van de Wiel, M.A., Van der Vaart, A.W. (2008), "A test for partial differential expression", Journal of the American Statistical Association, 103(483), 1039-1049.
RCMestimation
, RCMrandom
, rcmTest
.
# 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) # test for significance of effect of X on Y RCMtestResults <- RCMtest(Y, X, R, nBoot=2) summary(RCMtestResults)