View source: R/MultiLambdaCVfun.R
createXXblocks | R Documentation |
Creates list of (unscaled) sample covariance matrices X_b %*% t(X_b)
for data blocks b = 1,..., B.
createXXblocks(datablocks, datablocksnew = NULL, which2pair = NULL)
datablocks |
List of data frames or matrices |
datablocksnew |
List of data frames or matrices |
which2pair |
Integer vector of size 2 (or |
The efficiency of multiridge
for high-dimendional data relies largely on this function:
all iterative calculation are performed on the out put of this function, which contains B
blocks of
nxn
matrices. If which2pair != NULL
, the function adds a paired covariance block, pairing the two data blocks corresponding to the elements of which2pair
. If predictions for new samples are desired, one also needs to specify
datablocksnew
, which should have he exact same format as datablocks
with matching column dimension (number of variables).
List. Same number of component as datablocks
when which2pair = NULL
, or augmented with one paired data block.
Dimension is nxn
for all components.
createXblocks
, which is required when parameter estimates are desired (not needed for prediction). A full demo and data are available from:
https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4
#Example #Simulate Xbl1 <- matrix(rnorm(1000),nrow=10) Xbl2 <- matrix(rnorm(2000),nrow=10) #check whether dimensions are correct ncol(Xbl1)==nrow(Xbl2) #create cross-product XXbl <- createXXblocks(list(Xbl1,Xbl2)) #suppose penalties for two data types equal 5,10, respectively Sigma <- SigmaFromBlocks(XXbl,c(5,10)) #check dimensions (should be n x n) dim(Sigma)
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