regionSegCost {biomvRCNS} | R Documentation |
To calculate regional cost matrix for the initial stage and second merging stage of the segmentation model.
regionSegCost(x, maxk = NULL, segs = NULL, family = NULL, alpha = NULL, useSum = TRUE, useMC = FALSE, comVar = TRUE)
x |
The input data matrix or vector |
maxk |
Maximum number of index to search forward |
segs |
Starting indices (excluding 1) for the candidate segments, for the second stage model, |
family |
which exponential family the data belongs to, possible values are 'norm', 'pois' and 'nbinom' |
alpha |
alpha matrix for negative binomial cost calculation, estimated from |
useSum |
TRUE if using grand sum across sample / x columns, like in the |
useMC |
TRUE if |
comVar |
TRUE if assuming common variance across samples (x columns) |
Preparing the cost matrix for the follow-up segmentation. Using residual sum of squares for 'norm' data, and negative log-likelihood for 'pois' and 'nbinom' data.
Extension of the costMatrix
function in tilingArray
.
Matrix with maxk
rows and nrow(x)
columns, or a length(segs)+1
square matrix for the second stage model.
Piegorsch, W. W. (1990). Maximum likelihood estimation for the negative binomial dispersion parameter. Biometrics, 863-867.
Picard,F. et al. (2005) A statistical approach for array CGH data analysis. BMC Bioinformatics, 6, 27.
Huber,W. et al. (2006) Transcript mapping with high density oligonucleotide tiling arrays. Bioinformatics, 22, 1963-1970. .
Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
x<-matrix(rnorm(120), ncol=3) Ca<-regionSegCost(x, maxk=20, family='norm') dim(Ca) # [1] 20 40 Cb<-regionSegCost(x, segs=as.integer(c(3, 6, 12, 30)), family='norm') dim(Cb) # [1] 5 5