Running examples in 'bigpca-Ex.R' failed
The error most likely occurred in:

> ### Name: big.PCA
> ### Title: PCA/Singular Value Decomposition for big.matrix
> ### Aliases: big.PCA
> 
> ### ** Examples
> 
> # create an example matrix and its transpose
> min.dim <- 200; nvar <- 500; subset.size <- 50
> mat <- matrix(rnorm(min.dim*nvar),ncol=min.dim) 
> prv.large(mat)
Warning in check.bio() :
  bioconductor does not appear to be installed - this function works better if it is

      col# 
 row#        1        2  .....       200 
    1  -0.6265   0.0773   ...     0.0374 
    2   0.1836  -0.2969   ...    -1.6128 
    3  -0.8356  -1.1832   ...     0.7146 
  ...      ...      ...   ...        ... 
  500  -0.8733  -0.6973   ...     0.7118 
> t.mat <- t(mat)
> # create two alternative covariance matrices
> MMs <- t.mat %*% mat
> MsM <- mat %*% t.mat
> # run singular value decomposition
> pca <- svd(mat)   
> D <- pca$d # singular values (=sqrt(eigenvalues))
> V <- pca$v # right singular vector
> U <- pca$u # left singular vector
> sig <- mat-mat; diag(sig) <- D; 
> MMs2 <- V %*% (t(sig) %*% sig) %*% t(V)
> sig <- t.mat-t.mat; diag(sig) <- D; 
> MsM2 <- U %*% (sig %*% t(sig)) %*% t(U)
> # show that the covariance matrices are equal to the functions of 
> # the left and right singular vectors
> prv(MMs,MsM); prv(MMs2,MsM2)
MMs (matrix, 200*200)

      col# 
 row#          1          2  .....         200 
    1   511.2318   -22.5321   ...     -22.6612 
    2   -22.5321   558.8836   ...       12.462 
    3   -14.5398   -40.9556   ...     -32.9189 
  ...        ...        ...   ...          ... 
  200   -22.6612     12.462   ...     568.2967 

MsM (matrix, 500*500)

      col# 
 row#          1          2  .....         500 
    1   198.2148    -1.7057   ...      16.2122 
    2    -1.7057   241.7104   ...       0.4053 
    3   -14.7584     12.003   ...       4.6714 
  ...        ...        ...   ...          ... 
  500    16.2122     0.4053   ...     191.1251 

Warning messages:
1: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
2: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
3: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
4: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
MMs2 (matrix, 200*200)

      col# 
 row#          1          2  .....         200 
    1   511.2318   -22.5321   ...     -22.6612 
    2   -22.5321   558.8836   ...       12.462 
    3   -14.5398   -40.9556   ...     -32.9189 
  ...        ...        ...   ...          ... 
  200   -22.6612     12.462   ...     568.2967 

MsM2 (matrix, 500*500)

      col# 
 row#          1          2  .....         500 
    1   198.2148    -1.7057   ...      16.2122 
    2    -1.7057   241.7104   ...       0.4053 
    3   -14.7584     12.003   ...       4.6714 
  ...        ...        ...   ...          ... 
  500    16.2122     0.4053   ...     191.1251 

Warning messages:
1: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
2: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
3: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
4: In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
> pr <- princomp(mat) # PCA using eigendecomposition of cov matrix
> L <- matrix(rep(0,40000),ncol=200); diag(L) <- pr[[1]]^2 # eigenvalues as diag
> mat2 <- (pr[[2]]) %*% L %*%  solve(pr[[2]]) # = eigenvectors * eigenvalues * inv(eigenvectors)
> prv.large(cov(mat)); prv.large(mat2) #  == COVmat (may be slight tolerance differences)

      col# 
 row#        1        2  .....       200 
    1    1.024  -0.0441   ...     -0.045 
    2  -0.0441   1.1179   ...      0.024 
    3  -0.0291  -0.0822   ...     -0.066 
  ...      ...      ...   ...        ... 
  200   -0.045    0.024   ...     1.1385 
Warning message:
In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is

      col# 
 row#        1        2  .....       200 
    1    1.022   -0.044   ...    -0.0449 
    2   -0.044   1.1157   ...      0.024 
    3   -0.029   -0.082   ...    -0.0659 
  ...      ...      ...   ...        ... 
  200  -0.0449    0.024   ...     1.1362 
Warning message:
In check.bio() :
  bioconductor does not appear to be installed - this function works better if it is
> ## Now demonstrate the correlation between SVD and PCA ##
> # the right singular vector is highly correlated with the pca loadings:
> median(abs(diag(cor(V,pr[["loadings"]]))))
[1] 0.9834328
> # the left singular vector is highly correlated with the pca scores (eigenvectors):
> median(abs(diag(cor(U,pr[["scores"]]))))
[1] 0.9835682
> cor(pr$sdev,D) # the singular values are equivalent to the eigenvalues
[1] 0.9999933
> 
> ## MAIN EXAMPLES ##
> orig.dir <- getwd(); setwd(tempdir()); # move to temporary dir
> if(file.exists("testMyBig.bck")) { unlink(c("testMyBig.bck","testMyBig.dsc")) }
> bmat <- as.big.matrix(mat,backingfile="testMyBig.bck",
+  descriptorfile="testMyBig.dsc",  backingpath = getwd())
Error in filebacked.big.matrix(nrow = nrow, ncol = ncol, type = type,  : 
  Backing file already exists! Either remove or specify
             different backing file
Calls: as.big.matrix ... as.big.matrix -> big.matrix -> filebacked.big.matrix
Execution halted