Ben Bolstad
| Snapshot Date: 2025-11-12 13:40 -0500 (Wed, 12 Nov 2025) |
| git_url: https://git.bioconductor.org/packages/BufferedMatrix |
| git_branch: devel |
| git_last_commit: ecdbf23 |
| git_last_commit_date: 2025-10-29 09:58:55 -0500 (Wed, 29 Oct 2025) |
| Back to Build/check report for BioC 3.23: simplified long |
|
This page was generated on 2025-11-13 11:32 -0500 (Thu, 13 Nov 2025).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences" | 4013 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
| Package 251/2325 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| BufferedMatrix 1.75.0 (landing page) Ben Bolstad
| kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | WARNINGS | OK | | |||||||
|
To the developers/maintainers of the BufferedMatrix package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/BufferedMatrix.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: BufferedMatrix |
| Version: 1.75.0 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:BufferedMatrix.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings BufferedMatrix_1.75.0.tar.gz |
| StartedAt: 2025-11-12 18:09:56 -0500 (Wed, 12 Nov 2025) |
| EndedAt: 2025-11-12 18:10:16 -0500 (Wed, 12 Nov 2025) |
| EllapsedTime: 19.3 seconds |
| RetCode: 0 |
| Status: WARNINGS |
| CheckDir: BufferedMatrix.Rcheck |
| Warnings: 1 |
##############################################################################
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###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:BufferedMatrix.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings BufferedMatrix_1.75.0.tar.gz
###
##############################################################################
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* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck’
* using R Under development (unstable) (2025-11-04 r88984)
* using platform: aarch64-apple-darwin20
* R was compiled by
Apple clang version 16.0.0 (clang-1600.0.26.6)
GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.8
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘BufferedMatrix/DESCRIPTION’ ... OK
* this is package ‘BufferedMatrix’ version ‘1.75.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘BufferedMatrix’ can be installed ... WARNING
Found the following significant warnings:
doubleBufferedMatrix.c:1580:7: warning: logical not is only applied to the left hand side of this bitwise operator [-Wlogical-not-parentheses]
See ‘/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/00install.out’ for details.
* used C compiler: ‘Apple clang version 15.0.0 (clang-1500.1.0.2.5)’
* used SDK: ‘MacOSX11.3.1.sdk’
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) BufferedMatrix-class.Rd:209: Lost braces; missing escapes or markup?
209 | $x^{power}$ elementwise of the matrix
| ^
prepare_Rd: createBufferedMatrix.Rd:26: Dropping empty section \keyword
prepare_Rd: createBufferedMatrix.Rd:17-18: Dropping empty section \details
prepare_Rd: createBufferedMatrix.Rd:15-16: Dropping empty section \value
prepare_Rd: createBufferedMatrix.Rd:19-20: Dropping empty section \references
prepare_Rd: createBufferedMatrix.Rd:21-22: Dropping empty section \seealso
prepare_Rd: createBufferedMatrix.Rd:23-24: Dropping empty section \examples
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... INFO
Note: information on .o files is not available
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... NONE
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
Running ‘Rcodetesting.R’
Running ‘c_code_level_tests.R’
Running ‘objectTesting.R’
Running ‘rawCalltesting.R’
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 WARNING, 1 NOTE
See
‘/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/00check.log’
for details.
BufferedMatrix.Rcheck/00install.out
##############################################################################
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###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL BufferedMatrix
###
##############################################################################
##############################################################################
* installing to library ‘/Library/Frameworks/R.framework/Versions/4.6-arm64/Resources/library’
* installing *source* package ‘BufferedMatrix’ ...
** this is package ‘BufferedMatrix’ version ‘1.75.0’
** using staged installation
** libs
using C compiler: ‘Apple clang version 15.0.0 (clang-1500.1.0.2.5)’
using SDK: ‘MacOSX11.3.1.sdk’
clang -arch arm64 -std=gnu2x -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c RBufferedMatrix.c -o RBufferedMatrix.o
clang -arch arm64 -std=gnu2x -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c doubleBufferedMatrix.c -o doubleBufferedMatrix.o
doubleBufferedMatrix.c:1580:7: warning: logical not is only applied to the left hand side of this bitwise operator [-Wlogical-not-parentheses]
if (!(Matrix->readonly) & setting){
^ ~
doubleBufferedMatrix.c:1580:7: note: add parentheses after the '!' to evaluate the bitwise operator first
if (!(Matrix->readonly) & setting){
^
( )
doubleBufferedMatrix.c:1580:7: note: add parentheses around left hand side expression to silence this warning
if (!(Matrix->readonly) & setting){
^
( )
doubleBufferedMatrix.c:3327:12: warning: unused function 'sort_double' [-Wunused-function]
static int sort_double(const double *a1,const double *a2){
^
2 warnings generated.
clang -arch arm64 -std=gnu2x -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c doubleBufferedMatrix_C_tests.c -o doubleBufferedMatrix_C_tests.o
clang -arch arm64 -std=gnu2x -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c init_package.c -o init_package.o
clang -arch arm64 -std=gnu2x -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/arm64/lib -o BufferedMatrix.so RBufferedMatrix.o doubleBufferedMatrix.o doubleBufferedMatrix_C_tests.o init_package.o -F/Library/Frameworks/R.framework/.. -framework R
installing to /Library/Frameworks/R.framework/Versions/4.6-arm64/Resources/library/00LOCK-BufferedMatrix/00new/BufferedMatrix/libs
** R
** inst
** byte-compile and prepare package for lazy loading
Creating a new generic function for ‘rowMeans’ in package ‘BufferedMatrix’
Creating a new generic function for ‘rowSums’ in package ‘BufferedMatrix’
Creating a new generic function for ‘colMeans’ in package ‘BufferedMatrix’
Creating a new generic function for ‘colSums’ in package ‘BufferedMatrix’
Creating a generic function for ‘ncol’ from package ‘base’ in package ‘BufferedMatrix’
Creating a generic function for ‘nrow’ from package ‘base’ in package ‘BufferedMatrix’
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (BufferedMatrix)
BufferedMatrix.Rcheck/tests/c_code_level_tests.Rout
R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix", "BufferedMatrix", .libPaths());.C("dbm_c_tester",integer(1))
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
Adding Additional Column
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
0.000000 1.000000 2.000000 3.000000 4.000000 0.000000
1.000000 2.000000 3.000000 4.000000 5.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 0.000000
3.000000 4.000000 5.000000 6.000000 7.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 0.000000
Reassigning values
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Resizing Buffers
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 3
Buffer Cols: 3
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Activating Row Buffer
In row mode: 1
1.000000 6.000000 11.000000 16.000000 21.000000 26.000000
2.000000 7.000000 12.000000 17.000000 22.000000 27.000000
3.000000 8.000000 13.000000 18.000000 23.000000 28.000000
4.000000 9.000000 14.000000 19.000000 24.000000 29.000000
5.000000 10.000000 15.000000 20.000000 25.000000 30.000000
Squaring Last Column
1.000000 6.000000 11.000000 16.000000 21.000000 676.000000
2.000000 7.000000 12.000000 17.000000 22.000000 729.000000
3.000000 8.000000 13.000000 18.000000 23.000000 784.000000
4.000000 9.000000 14.000000 19.000000 24.000000 841.000000
5.000000 10.000000 15.000000 20.000000 25.000000 900.000000
Square rooting Last Row, then turing off Row Buffer
In row mode: 0
Checking on value that should be not be in column buffer2.236068
1.000000 6.000000 11.000000 16.000000 21.000000 676.000000
2.000000 7.000000 12.000000 17.000000 22.000000 729.000000
3.000000 8.000000 13.000000 18.000000 23.000000 784.000000
4.000000 9.000000 14.000000 19.000000 24.000000 841.000000
2.236068 3.162278 3.872983 4.472136 5.000000 30.000000
Single Indexing. Assign each value its square
1.000000 36.000000 121.000000 256.000000 441.000000 676.000000
4.000000 49.000000 144.000000 289.000000 484.000000 729.000000
9.000000 64.000000 169.000000 324.000000 529.000000 784.000000
16.000000 81.000000 196.000000 361.000000 576.000000 841.000000
25.000000 100.000000 225.000000 400.000000 625.000000 900.000000
Resizing Buffers Smaller
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
1.000000 36.000000 121.000000 256.000000 441.000000 676.000000
4.000000 49.000000 144.000000 289.000000 484.000000 729.000000
9.000000 64.000000 169.000000 324.000000 529.000000 784.000000
16.000000 81.000000 196.000000 361.000000 576.000000 841.000000
25.000000 100.000000 225.000000 400.000000 625.000000 900.000000
Activating Row Mode.
Resizing Buffers
Checking dimensions
Rows: 5
Cols: 6
Buffer Rows: 1
Buffer Cols: 1
Activating ReadOnly Mode.
The results of assignment is: 0
Printing matrix reversed.
900.000000 625.000000 400.000000 225.000000 100.000000 25.000000
841.000000 576.000000 361.000000 196.000000 81.000000 16.000000
784.000000 529.000000 324.000000 169.000000 64.000000 9.000000
729.000000 484.000000 289.000000 144.000000 49.000000 -30.000000
676.000000 441.000000 256.000000 121.000000 -20.000000 -10.000000
[[1]]
[1] 0
>
> proc.time()
user system elapsed
0.125 0.049 0.175
BufferedMatrix.Rcheck/tests/objectTesting.Rout
R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
>
> ### this is used to control how many repetitions in something below
> ### higher values result in more checks.
> nreps <-100 ##20000
>
>
> ## test creation and some simple assignments and subsetting operations
>
> ## first on single elements
> tmp <- createBufferedMatrix(1000,10)
>
> tmp[10,5]
[1] 0
> tmp[10,5] <- 10
> tmp[10,5]
[1] 10
> tmp[10,5] <- 12.445
> tmp[10,5]
[1] 12.445
>
>
>
> ## now testing accessing multiple elements
> tmp2 <- createBufferedMatrix(10,20)
>
>
> tmp2[3,1] <- 51.34
> tmp2[9,2] <- 9.87654
> tmp2[,1:2]
[,1] [,2]
[1,] 0.00 0.00000
[2,] 0.00 0.00000
[3,] 51.34 0.00000
[4,] 0.00 0.00000
[5,] 0.00 0.00000
[6,] 0.00 0.00000
[7,] 0.00 0.00000
[8,] 0.00 0.00000
[9,] 0.00 9.87654
[10,] 0.00 0.00000
> tmp2[,-(3:20)]
[,1] [,2]
[1,] 0.00 0.00000
[2,] 0.00 0.00000
[3,] 51.34 0.00000
[4,] 0.00 0.00000
[5,] 0.00 0.00000
[6,] 0.00 0.00000
[7,] 0.00 0.00000
[8,] 0.00 0.00000
[9,] 0.00 9.87654
[10,] 0.00 0.00000
> tmp2[3,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 51.34 0 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 0 0 0 0 0 0 0
> tmp2[-3,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 9.87654 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0.00000 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
[1,] 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0
> tmp2[2,1:3]
[,1] [,2] [,3]
[1,] 0 0 0
> tmp2[3:9,1:3]
[,1] [,2] [,3]
[1,] 51.34 0.00000 0
[2,] 0.00 0.00000 0
[3,] 0.00 0.00000 0
[4,] 0.00 0.00000 0
[5,] 0.00 0.00000 0
[6,] 0.00 0.00000 0
[7,] 0.00 9.87654 0
> tmp2[-4,-4]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[2,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[3,] 51.34 0.00000 0 0 0 0 0 0 0 0 0 0 0
[4,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[5,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[6,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[7,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[8,] 0.00 9.87654 0 0 0 0 0 0 0 0 0 0 0
[9,] 0.00 0.00000 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19]
[1,] 0 0 0 0 0 0
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0
[7,] 0 0 0 0 0 0
[8,] 0 0 0 0 0 0
[9,] 0 0 0 0 0 0
>
> ## now testing accessing/assigning multiple elements
> tmp3 <- createBufferedMatrix(10,10)
>
> for (i in 1:10){
+ for (j in 1:10){
+ tmp3[i,j] <- (j-1)*10 + i
+ }
+ }
>
> tmp3[2:4,2:4]
[,1] [,2] [,3]
[1,] 12 22 32
[2,] 13 23 33
[3,] 14 24 34
> tmp3[c(-10),c(2:4,2:4,10,1,2,1:10,10:1)]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 11 21 31 11 21 31 91 1 11 1 11 21 31
[2,] 12 22 32 12 22 32 92 2 12 2 12 22 32
[3,] 13 23 33 13 23 33 93 3 13 3 13 23 33
[4,] 14 24 34 14 24 34 94 4 14 4 14 24 34
[5,] 15 25 35 15 25 35 95 5 15 5 15 25 35
[6,] 16 26 36 16 26 36 96 6 16 6 16 26 36
[7,] 17 27 37 17 27 37 97 7 17 7 17 27 37
[8,] 18 28 38 18 28 38 98 8 18 8 18 28 38
[9,] 19 29 39 19 29 39 99 9 19 9 19 29 39
[,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
[1,] 41 51 61 71 81 91 91 81 71 61 51 41
[2,] 42 52 62 72 82 92 92 82 72 62 52 42
[3,] 43 53 63 73 83 93 93 83 73 63 53 43
[4,] 44 54 64 74 84 94 94 84 74 64 54 44
[5,] 45 55 65 75 85 95 95 85 75 65 55 45
[6,] 46 56 66 76 86 96 96 86 76 66 56 46
[7,] 47 57 67 77 87 97 97 87 77 67 57 47
[8,] 48 58 68 78 88 98 98 88 78 68 58 48
[9,] 49 59 69 79 89 99 99 89 79 69 59 49
[,26] [,27] [,28] [,29]
[1,] 31 21 11 1
[2,] 32 22 12 2
[3,] 33 23 13 3
[4,] 34 24 14 4
[5,] 35 25 15 5
[6,] 36 26 16 6
[7,] 37 27 17 7
[8,] 38 28 18 8
[9,] 39 29 19 9
> tmp3[-c(1:5),-c(6:10)]
[,1] [,2] [,3] [,4] [,5]
[1,] 6 16 26 36 46
[2,] 7 17 27 37 47
[3,] 8 18 28 38 48
[4,] 9 19 29 39 49
[5,] 10 20 30 40 50
>
> ## assignment of whole columns
> tmp3[,1] <- c(1:10*100.0)
> tmp3[,1:2] <- tmp3[,1:2]*100
> tmp3[,1:2] <- tmp3[,2:1]
> tmp3[,1:2]
[,1] [,2]
[1,] 1100 1e+04
[2,] 1200 2e+04
[3,] 1300 3e+04
[4,] 1400 4e+04
[5,] 1500 5e+04
[6,] 1600 6e+04
[7,] 1700 7e+04
[8,] 1800 8e+04
[9,] 1900 9e+04
[10,] 2000 1e+05
>
>
> tmp3[,-1] <- tmp3[,1:9]
> tmp3[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1100 1100 1e+04 21 31 41 51 61 71 81
[2,] 1200 1200 2e+04 22 32 42 52 62 72 82
[3,] 1300 1300 3e+04 23 33 43 53 63 73 83
[4,] 1400 1400 4e+04 24 34 44 54 64 74 84
[5,] 1500 1500 5e+04 25 35 45 55 65 75 85
[6,] 1600 1600 6e+04 26 36 46 56 66 76 86
[7,] 1700 1700 7e+04 27 37 47 57 67 77 87
[8,] 1800 1800 8e+04 28 38 48 58 68 78 88
[9,] 1900 1900 9e+04 29 39 49 59 69 79 89
[10,] 2000 2000 1e+05 30 40 50 60 70 80 90
>
> tmp3[,1:2] <- rep(1,10)
> tmp3[,1:2] <- rep(1,20)
> tmp3[,1:2] <- matrix(c(1:5),1,5)
>
> tmp3[,-c(1:8)] <- matrix(c(1:5),1,5)
>
> tmp3[1,] <- 1:10
> tmp3[1,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
> tmp3[-1,] <- c(1,2)
> tmp3[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
[2,] 1 2 1 2 1 2 1 2 1 2
[3,] 2 1 2 1 2 1 2 1 2 1
[4,] 1 2 1 2 1 2 1 2 1 2
[5,] 2 1 2 1 2 1 2 1 2 1
[6,] 1 2 1 2 1 2 1 2 1 2
[7,] 2 1 2 1 2 1 2 1 2 1
[8,] 1 2 1 2 1 2 1 2 1 2
[9,] 2 1 2 1 2 1 2 1 2 1
[10,] 1 2 1 2 1 2 1 2 1 2
> tmp3[-c(1:8),] <- matrix(c(1:5),1,5)
> tmp3[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 2 3 4 5 6 7 8 9 10
[2,] 1 2 1 2 1 2 1 2 1 2
[3,] 2 1 2 1 2 1 2 1 2 1
[4,] 1 2 1 2 1 2 1 2 1 2
[5,] 2 1 2 1 2 1 2 1 2 1
[6,] 1 2 1 2 1 2 1 2 1 2
[7,] 2 1 2 1 2 1 2 1 2 1
[8,] 1 2 1 2 1 2 1 2 1 2
[9,] 1 3 5 2 4 1 3 5 2 4
[10,] 2 4 1 3 5 2 4 1 3 5
>
>
> tmp3[1:2,1:2] <- 5555.04
> tmp3[-(1:2),1:2] <- 1234.56789
>
>
>
> ## testing accessors for the directory and prefix
> directory(tmp3)
[1] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests"
> prefix(tmp3)
[1] "BM"
>
> ## testing if we can remove these objects
> rm(tmp, tmp2, tmp3)
> gc()
used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 481248 25.8 1058085 56.6 NA 633817 33.9
Vcells 891449 6.9 8388608 64.0 196608 2110969 16.2
>
>
>
>
> ##
> ## checking reads
> ##
>
> tmp2 <- createBufferedMatrix(10,20)
>
> test.sample <- rnorm(10*20)
>
> tmp2[1:10,1:20] <- test.sample
>
> test.matrix <- matrix(test.sample,10,20)
>
> ## testing reads
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ which.col <- sample(1:20,1)
+ if (tmp2[which.row,which.col] != test.matrix[which.row,which.col]){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> date()
[1] "Wed Nov 12 18:10:06 2025"
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
> date()
[1] "Wed Nov 12 18:10:06 2025"
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,which.col] == test.matrix[which.row,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
>
>
> RowMode(tmp2)
<pointer: 0x600000f0c120>
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ which.col <- sample(1:20,1)
+ if (tmp2[which.row,which.col] != test.matrix[which.row,which.col]){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,5,replace=TRUE)
+ if (!all(tmp2[,which.col] == test.matrix[,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ if (!all(tmp2[which.row,] == test.matrix[which.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
>
>
> date()
[1] "Wed Nov 12 18:10:08 2025"
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:20,5,replace=TRUE)
+ if (!all(tmp2[which.row,which.col] == test.matrix[which.row,which.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
> date()
[1] "Wed Nov 12 18:10:09 2025"
>
> ColMode(tmp2)
<pointer: 0x600000f0c120>
>
>
>
> ### Now testing assignments
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,1)
+
+ new.data <- rnorm(20)
+ tmp2[which.row,] <- new.data
+ test.matrix[which.row,] <- new.data
+ if (rep > 1){
+ if (!all(tmp2[prev.row,] == test.matrix[prev.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,1)
+ new.data <- rnorm(10)
+ tmp2[,which.col] <- new.data
+ test.matrix[,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[,prev.col] == test.matrix[,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.col <- which.col
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.col <- sample(1:20,5,replace=TRUE)
+ new.data <- matrix(rnorm(50),5,10)
+ tmp2[,which.col] <- new.data
+ test.matrix[,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[,prev.col] == test.matrix[,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.col <- which.col
+ }
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ new.data <- matrix(rnorm(50),5,10)
+ tmp2[which.row,] <- new.data
+ test.matrix[which.row,]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[prev.row,] == test.matrix[prev.row,])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+ }
>
>
>
>
>
> for (rep in 1:nreps){
+ which.row <- sample(1:10,5,replace=TRUE)
+ which.col <- sample(1:20,5,replace=TRUE)
+ new.data <- matrix(rnorm(25),5,5)
+ tmp2[which.row,which.col] <- new.data
+ test.matrix[which.row,which.col]<- new.data
+
+ if (rep > 1){
+ if (!all(tmp2[prev.row,prev.col] == test.matrix[prev.row,prev.col])){
+ cat("incorrect agreement")
+ break;
+ }
+ }
+ prev.row <- which.row
+ prev.col <- which.col
+ }
>
>
>
>
> ###
> ###
> ### testing some more functions
> ###
>
>
>
> ## duplication function
> tmp5 <- duplicate(tmp2)
>
> # making sure really did copy everything.
> tmp5[1,1] <- tmp5[1,1] +100.00
>
> if (tmp5[1,1] == tmp2[1,1]){
+ stop("Problem with duplication")
+ }
>
>
>
>
> ### testing elementwise applying of functions
>
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 100.4937346 -0.9403866 -0.1917917 -0.09641765
[2,] -0.2410684 -0.1119747 0.5816657 -0.62278272
[3,] 1.6013982 -0.5840941 -0.0156508 0.27551486
[4,] -1.6414175 -0.5696074 -1.2020233 0.43262263
> ewApply(tmp5,abs)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 100.4937346 0.9403866 0.1917917 0.09641765
[2,] 0.2410684 0.1119747 0.5816657 0.62278272
[3,] 1.6013982 0.5840941 0.0156508 0.27551486
[4,] 1.6414175 0.5696074 1.2020233 0.43262263
> ewApply(tmp5,sqrt)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 10.0246563 0.9697353 0.4379403 0.3105119
[2,] 0.4909871 0.3346262 0.7626701 0.7891658
[3,] 1.2654636 0.7642605 0.1251032 0.5248951
[4,] 1.2811782 0.7547234 1.0963682 0.6577405
>
> my.function <- function(x,power){
+ (x+5)^power
+ }
>
> ewApply(tmp5,my.function,power=2)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 2 Kilobytes.
Disk usage : 1.6 Kilobytes.
> tmp5[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 225.74030 35.63774 29.57119 28.20154
[2,] 30.15094 28.45824 33.20837 33.51444
[3,] 39.25603 33.22670 26.26668 30.52447
[4,] 39.45320 33.11684 37.16571 32.01003
>
>
>
> ## testing functions that elementwise transform the matrix
> sqrt(tmp5)
<pointer: 0x600000f24000>
> exp(tmp5)
<pointer: 0x600000f24000>
> log(tmp5,2)
<pointer: 0x600000f24000>
> pow(tmp5,2)
>
>
>
>
>
> ## testing functions that apply to entire matrix
> Max(tmp5)
[1] 469.8489
> Min(tmp5)
[1] 54.67066
> mean(tmp5)
[1] 72.65978
> Sum(tmp5)
[1] 14531.96
> Var(tmp5)
[1] 860.1476
>
>
> ## testing functions applied to rows or columns
>
> rowMeans(tmp5)
[1] 90.13400 70.04618 70.66556 72.68630 69.53042 73.85371 70.83405 67.56998
[9] 69.75391 71.52367
> rowSums(tmp5)
[1] 1802.680 1400.924 1413.311 1453.726 1390.608 1477.074 1416.681 1351.400
[9] 1395.078 1430.473
> rowVars(tmp5)
[1] 8064.35265 59.35016 56.00860 42.07955 86.36541 69.12270
[7] 76.99060 28.32572 75.51475 65.17330
> rowSd(tmp5)
[1] 89.801741 7.703905 7.483889 6.486875 9.293299 8.314006 8.774428
[8] 5.322192 8.689922 8.072998
> rowMax(tmp5)
[1] 469.84885 87.65470 82.22118 82.11666 87.02375 90.38039 86.25273
[8] 81.81506 85.67822 88.44465
> rowMin(tmp5)
[1] 55.89462 57.47624 54.67066 60.07550 55.14355 60.89433 58.11473 57.41458
[9] 55.11693 55.15343
>
> colMeans(tmp5)
[1] 113.84246 68.62596 67.58667 71.13400 70.59651 74.20714 69.67477
[8] 70.65997 67.63754 71.17028 72.27947 74.09173 71.19183 71.15704
[15] 66.58670 71.78737 68.44746 67.62183 74.81939 70.07744
> colSums(tmp5)
[1] 1138.4246 686.2596 675.8667 711.3400 705.9651 742.0714 696.7477
[8] 706.5997 676.3754 711.7028 722.7947 740.9173 711.9183 711.5704
[15] 665.8670 717.8737 684.4746 676.2183 748.1939 700.7744
> colVars(tmp5)
[1] 15715.27846 56.56791 102.97766 101.82687 34.43113 76.80948
[7] 31.83788 86.61589 42.26228 43.90288 73.09788 19.52045
[13] 110.31259 81.54138 70.52755 74.23836 28.17269 43.95156
[19] 69.52832 59.79620
> colSd(tmp5)
[1] 125.360594 7.521164 10.147791 10.090930 5.867805 8.764102
[7] 5.642506 9.306766 6.500945 6.625925 8.549730 4.418196
[13] 10.502980 9.030026 8.398068 8.616168 5.307796 6.629597
[19] 8.338364 7.732800
> colMax(tmp5)
[1] 469.84885 85.84377 87.82098 86.25273 77.04126 85.67822 74.94728
[8] 87.02375 80.11984 81.35787 82.91201 82.05015 88.44465 84.40310
[15] 80.96711 83.32325 78.36650 76.37809 87.65470 80.06281
> colMin(tmp5)
[1] 62.75523 59.23209 54.67066 58.69780 60.07550 61.99032 57.88680 58.11473
[9] 60.89433 61.72417 58.34872 67.89201 55.14355 55.15343 55.11693 58.97124
[17] 57.41458 55.89462 62.92957 56.12108
>
>
> ### setting a random element to NA and then testing with na.rm=TRUE or na.rm=FALSE (The default)
>
>
> which.row <- sample(1:10,1,replace=TRUE)
> which.col <- sample(1:20,1,replace=TRUE)
>
> tmp5[which.row,which.col] <- NA
>
> Max(tmp5)
[1] NA
> Min(tmp5)
[1] NA
> mean(tmp5)
[1] NA
> Sum(tmp5)
[1] NA
> Var(tmp5)
[1] NA
>
> rowMeans(tmp5)
[1] 90.13400 70.04618 70.66556 72.68630 69.53042 73.85371 NA 67.56998
[9] 69.75391 71.52367
> rowSums(tmp5)
[1] 1802.680 1400.924 1413.311 1453.726 1390.608 1477.074 NA 1351.400
[9] 1395.078 1430.473
> rowVars(tmp5)
[1] 8064.35265 59.35016 56.00860 42.07955 86.36541 69.12270
[7] 78.39302 28.32572 75.51475 65.17330
> rowSd(tmp5)
[1] 89.801741 7.703905 7.483889 6.486875 9.293299 8.314006 8.853983
[8] 5.322192 8.689922 8.072998
> rowMax(tmp5)
[1] 469.84885 87.65470 82.22118 82.11666 87.02375 90.38039 NA
[8] 81.81506 85.67822 88.44465
> rowMin(tmp5)
[1] 55.89462 57.47624 54.67066 60.07550 55.14355 60.89433 NA 57.41458
[9] 55.11693 55.15343
>
> colMeans(tmp5)
[1] 113.84246 68.62596 67.58667 71.13400 70.59651 74.20714 69.67477
[8] 70.65997 67.63754 71.17028 72.27947 74.09173 71.19183 71.15704
[15] 66.58670 71.78737 68.44746 67.62183 74.81939 NA
> colSums(tmp5)
[1] 1138.4246 686.2596 675.8667 711.3400 705.9651 742.0714 696.7477
[8] 706.5997 676.3754 711.7028 722.7947 740.9173 711.9183 711.5704
[15] 665.8670 717.8737 684.4746 676.2183 748.1939 NA
> colVars(tmp5)
[1] 15715.27846 56.56791 102.97766 101.82687 34.43113 76.80948
[7] 31.83788 86.61589 42.26228 43.90288 73.09788 19.52045
[13] 110.31259 81.54138 70.52755 74.23836 28.17269 43.95156
[19] 69.52832 NA
> colSd(tmp5)
[1] 125.360594 7.521164 10.147791 10.090930 5.867805 8.764102
[7] 5.642506 9.306766 6.500945 6.625925 8.549730 4.418196
[13] 10.502980 9.030026 8.398068 8.616168 5.307796 6.629597
[19] 8.338364 NA
> colMax(tmp5)
[1] 469.84885 85.84377 87.82098 86.25273 77.04126 85.67822 74.94728
[8] 87.02375 80.11984 81.35787 82.91201 82.05015 88.44465 84.40310
[15] 80.96711 83.32325 78.36650 76.37809 87.65470 NA
> colMin(tmp5)
[1] 62.75523 59.23209 54.67066 58.69780 60.07550 61.99032 57.88680 58.11473
[9] 60.89433 61.72417 58.34872 67.89201 55.14355 55.15343 55.11693 58.97124
[17] 57.41458 55.89462 62.92957 NA
>
> Max(tmp5,na.rm=TRUE)
[1] 469.8489
> Min(tmp5,na.rm=TRUE)
[1] 54.67066
> mean(tmp5,na.rm=TRUE)
[1] 72.63372
> Sum(tmp5,na.rm=TRUE)
[1] 14454.11
> Var(tmp5,na.rm=TRUE)
[1] 864.3553
>
> rowMeans(tmp5,na.rm=TRUE)
[1] 90.13400 70.04618 70.66556 72.68630 69.53042 73.85371 70.46503 67.56998
[9] 69.75391 71.52367
> rowSums(tmp5,na.rm=TRUE)
[1] 1802.680 1400.924 1413.311 1453.726 1390.608 1477.074 1338.836 1351.400
[9] 1395.078 1430.473
> rowVars(tmp5,na.rm=TRUE)
[1] 8064.35265 59.35016 56.00860 42.07955 86.36541 69.12270
[7] 78.39302 28.32572 75.51475 65.17330
> rowSd(tmp5,na.rm=TRUE)
[1] 89.801741 7.703905 7.483889 6.486875 9.293299 8.314006 8.853983
[8] 5.322192 8.689922 8.072998
> rowMax(tmp5,na.rm=TRUE)
[1] 469.84885 87.65470 82.22118 82.11666 87.02375 90.38039 86.25273
[8] 81.81506 85.67822 88.44465
> rowMin(tmp5,na.rm=TRUE)
[1] 55.89462 57.47624 54.67066 60.07550 55.14355 60.89433 58.11473 57.41458
[9] 55.11693 55.15343
>
> colMeans(tmp5,na.rm=TRUE)
[1] 113.84246 68.62596 67.58667 71.13400 70.59651 74.20714 69.67477
[8] 70.65997 67.63754 71.17028 72.27947 74.09173 71.19183 71.15704
[15] 66.58670 71.78737 68.44746 67.62183 74.81939 69.21433
> colSums(tmp5,na.rm=TRUE)
[1] 1138.4246 686.2596 675.8667 711.3400 705.9651 742.0714 696.7477
[8] 706.5997 676.3754 711.7028 722.7947 740.9173 711.9183 711.5704
[15] 665.8670 717.8737 684.4746 676.2183 748.1939 622.9290
> colVars(tmp5,na.rm=TRUE)
[1] 15715.27846 56.56791 102.97766 101.82687 34.43113 76.80948
[7] 31.83788 86.61589 42.26228 43.90288 73.09788 19.52045
[13] 110.31259 81.54138 70.52755 74.23836 28.17269 43.95156
[19] 69.52832 58.88993
> colSd(tmp5,na.rm=TRUE)
[1] 125.360594 7.521164 10.147791 10.090930 5.867805 8.764102
[7] 5.642506 9.306766 6.500945 6.625925 8.549730 4.418196
[13] 10.502980 9.030026 8.398068 8.616168 5.307796 6.629597
[19] 8.338364 7.673978
> colMax(tmp5,na.rm=TRUE)
[1] 469.84885 85.84377 87.82098 86.25273 77.04126 85.67822 74.94728
[8] 87.02375 80.11984 81.35787 82.91201 82.05015 88.44465 84.40310
[15] 80.96711 83.32325 78.36650 76.37809 87.65470 80.06281
> colMin(tmp5,na.rm=TRUE)
[1] 62.75523 59.23209 54.67066 58.69780 60.07550 61.99032 57.88680 58.11473
[9] 60.89433 61.72417 58.34872 67.89201 55.14355 55.15343 55.11693 58.97124
[17] 57.41458 55.89462 62.92957 56.12108
>
> # now set an entire row to NA
>
> tmp5[which.row,] <- NA
> rowMeans(tmp5,na.rm=TRUE)
[1] 90.13400 70.04618 70.66556 72.68630 69.53042 73.85371 NaN 67.56998
[9] 69.75391 71.52367
> rowSums(tmp5,na.rm=TRUE)
[1] 1802.680 1400.924 1413.311 1453.726 1390.608 1477.074 0.000 1351.400
[9] 1395.078 1430.473
> rowVars(tmp5,na.rm=TRUE)
[1] 8064.35265 59.35016 56.00860 42.07955 86.36541 69.12270
[7] NA 28.32572 75.51475 65.17330
> rowSd(tmp5,na.rm=TRUE)
[1] 89.801741 7.703905 7.483889 6.486875 9.293299 8.314006 NA
[8] 5.322192 8.689922 8.072998
> rowMax(tmp5,na.rm=TRUE)
[1] 469.84885 87.65470 82.22118 82.11666 87.02375 90.38039 NA
[8] 81.81506 85.67822 88.44465
> rowMin(tmp5,na.rm=TRUE)
[1] 55.89462 57.47624 54.67066 60.07550 55.14355 60.89433 NA 57.41458
[9] 55.11693 55.15343
>
>
> # now set an entire col to NA
>
>
> tmp5[,which.col] <- NA
> colMeans(tmp5,na.rm=TRUE)
[1] 118.57803 66.71287 68.26062 69.45414 71.70682 75.55958 70.27951
[8] 72.05389 68.36698 70.03833 72.71907 73.81496 71.44758 71.66360
[15] 64.98888 71.71690 68.65215 67.69801 74.32649 NaN
> colSums(tmp5,na.rm=TRUE)
[1] 1067.2023 600.4158 614.3456 625.0873 645.3613 680.0362 632.5156
[8] 648.4850 615.3028 630.3449 654.4716 664.3346 643.0282 644.9724
[15] 584.8999 645.4521 617.8693 609.2821 668.9384 0.0000
> colVars(tmp5,na.rm=TRUE)
[1] 17427.39933 22.46486 110.74007 82.80855 24.86632 65.83331
[7] 31.70335 75.58413 41.55911 34.97590 80.06106 21.09875
[13] 123.36581 88.84722 50.62179 83.46229 31.22292 49.38022
[19] 75.48619 NA
> colSd(tmp5,na.rm=TRUE)
[1] 132.012876 4.739711 10.523311 9.099921 4.986614 8.113773
[7] 5.630573 8.693913 6.446636 5.914042 8.947684 4.593338
[13] 11.107016 9.425881 7.114899 9.135770 5.587748 7.027106
[19] 8.688279 NA
> colMax(tmp5,na.rm=TRUE)
[1] 469.84885 74.17529 87.82098 85.35839 77.04126 85.67822 74.94728
[8] 87.02375 80.11984 79.19474 82.91201 82.05015 88.44465 84.40310
[15] 75.08358 83.32325 78.36650 76.37809 87.65470 -Inf
> colMin(tmp5,na.rm=TRUE)
[1] 62.75523 59.23209 54.67066 58.69780 60.07550 61.99032 57.88680 61.39332
[9] 60.89433 61.72417 58.34872 67.89201 55.14355 55.15343 55.11693 58.97124
[17] 57.41458 55.89462 62.92957 Inf
>
>
>
>
> copymatrix <- matrix(rnorm(200,150,15),10,20)
>
> tmp5[1:10,1:20] <- copymatrix
> which.row <- 3
> which.col <- 1
> cat(which.row," ",which.col,"\n")
3 1
> tmp5[which.row,which.col] <- NA
> copymatrix[which.row,which.col] <- NA
>
> rowVars(tmp5,na.rm=TRUE)
[1] 231.8082 217.9008 208.8149 150.7107 145.7122 215.6527 272.1450 319.3596
[9] 211.4767 187.8241
> apply(copymatrix,1,var,na.rm=TRUE)
[1] 231.8082 217.9008 208.8149 150.7107 145.7122 215.6527 272.1450 319.3596
[9] 211.4767 187.8241
>
>
>
> copymatrix <- matrix(rnorm(200,150,15),10,20)
>
> tmp5[1:10,1:20] <- copymatrix
> which.row <- 1
> which.col <- 3
> cat(which.row," ",which.col,"\n")
1 3
> tmp5[which.row,which.col] <- NA
> copymatrix[which.row,which.col] <- NA
>
> colVars(tmp5,na.rm=TRUE)-apply(copymatrix,2,var,na.rm=TRUE)
[1] -1.705303e-13 -5.684342e-14 0.000000e+00 0.000000e+00 1.136868e-13
[6] -8.526513e-14 8.526513e-14 -4.263256e-14 5.684342e-14 1.705303e-13
[11] 8.526513e-14 5.684342e-14 -5.684342e-14 5.684342e-14 4.547474e-13
[16] 0.000000e+00 5.684342e-14 5.684342e-14 -1.705303e-13 -2.842171e-14
>
>
>
>
>
>
>
>
>
>
> ## making sure these things agree
> ##
> ## first when there is no NA
>
>
>
> agree.checks <- function(buff.matrix,r.matrix,err.tol=1e-10){
+
+ if (Max(buff.matrix,na.rm=TRUE) != max(r.matrix,na.rm=TRUE)){
+ stop("No agreement in Max")
+ }
+
+
+ if (Min(buff.matrix,na.rm=TRUE) != min(r.matrix,na.rm=TRUE)){
+ stop("No agreement in Min")
+ }
+
+
+ if (abs(Sum(buff.matrix,na.rm=TRUE)- sum(r.matrix,na.rm=TRUE)) > err.tol){
+
+ cat(Sum(buff.matrix,na.rm=TRUE),"\n")
+ cat(sum(r.matrix,na.rm=TRUE),"\n")
+ cat(Sum(buff.matrix,na.rm=TRUE) - sum(r.matrix,na.rm=TRUE),"\n")
+
+ stop("No agreement in Sum")
+ }
+
+ if (abs(mean(buff.matrix,na.rm=TRUE) - mean(r.matrix,na.rm=TRUE)) > err.tol){
+ stop("No agreement in mean")
+ }
+
+
+ if(abs(Var(buff.matrix,na.rm=TRUE) - var(as.vector(r.matrix),na.rm=TRUE)) > err.tol){
+ stop("No agreement in Var")
+ }
+
+
+
+ if(any(abs(rowMeans(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,mean,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowMeans")
+ }
+
+
+ if(any(abs(colMeans(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,mean,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in colMeans")
+ }
+
+
+ if(any(abs(rowSums(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,sum,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in rowSums")
+ }
+
+
+ if(any(abs(colSums(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,sum,na.rm=TRUE))> err.tol,na.rm=TRUE)){
+ stop("No agreement in colSums")
+ }
+
+ ### this is to get around the fact that R doesn't like to compute NA on an entire vector of NA when
+ ### computing variance
+ my.Var <- function(x,na.rm=FALSE){
+ if (all(is.na(x))){
+ return(NA)
+ } else {
+ var(x,na.rm=na.rm)
+ }
+
+ }
+
+ if(any(abs(rowVars(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,my.Var,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowVars")
+ }
+
+
+ if(any(abs(colVars(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,my.Var,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in rowVars")
+ }
+
+
+ if(any(abs(rowMax(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,max,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMax")
+ }
+
+
+ if(any(abs(colMax(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,max,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMax")
+ }
+
+
+
+ if(any(abs(rowMin(buff.matrix,na.rm=TRUE) - apply(r.matrix,1,min,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMin")
+ }
+
+
+ if(any(abs(colMin(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,min,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMin")
+ }
+
+ if(any(abs(colMedians(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,median,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colMedian")
+ }
+
+ if(any(abs(colRanges(buff.matrix,na.rm=TRUE) - apply(r.matrix,2,range,na.rm=TRUE)) > err.tol,na.rm=TRUE)){
+ stop("No agreement in colRanges")
+ }
+
+
+
+ }
>
>
>
>
>
>
>
>
>
> for (rep in 1:20){
+ copymatrix <- matrix(rnorm(200,150,15),10,20)
+
+ tmp5[1:10,1:20] <- copymatrix
+
+
+ agree.checks(tmp5,copymatrix)
+
+ ## now lets assign some NA values and check agreement
+
+ which.row <- sample(1:10,1,replace=TRUE)
+ which.col <- sample(1:20,1,replace=TRUE)
+
+ cat(which.row," ",which.col,"\n")
+
+ tmp5[which.row,which.col] <- NA
+ copymatrix[which.row,which.col] <- NA
+
+ agree.checks(tmp5,copymatrix)
+
+ ## make an entire row NA
+ tmp5[which.row,] <- NA
+ copymatrix[which.row,] <- NA
+
+
+ agree.checks(tmp5,copymatrix)
+
+ ### also make an entire col NA
+ tmp5[,which.col] <- NA
+ copymatrix[,which.col] <- NA
+
+ agree.checks(tmp5,copymatrix)
+
+ ### now make 1 element non NA with NA in the rest of row and column
+
+ tmp5[which.row,which.col] <- rnorm(1,150,15)
+ copymatrix[which.row,which.col] <- tmp5[which.row,which.col]
+
+ agree.checks(tmp5,copymatrix)
+ }
9 17
1 19
8 7
8 15
7 3
5 8
5 20
10 10
10 8
2 5
3 17
10 10
6 10
10 7
9 15
5 16
8 10
6 4
7 7
1 13
There were 50 or more warnings (use warnings() to see the first 50)
>
>
> ### now test 1 by n and n by 1 matrix
>
>
> err.tol <- 1e-12
>
> rm(tmp5)
>
> dataset1 <- rnorm(100)
> dataset2 <- rnorm(100)
>
> tmp <- createBufferedMatrix(1,100)
> tmp[1,] <- dataset1
>
> tmp2 <- createBufferedMatrix(100,1)
> tmp2[,1] <- dataset2
>
>
>
>
>
> Max(tmp)
[1] 2.827186
> Min(tmp)
[1] -2.357118
> mean(tmp)
[1] 0.136671
> Sum(tmp)
[1] 13.6671
> Var(tmp)
[1] 0.9566064
>
> rowMeans(tmp)
[1] 0.136671
> rowSums(tmp)
[1] 13.6671
> rowVars(tmp)
[1] 0.9566064
> rowSd(tmp)
[1] 0.9780626
> rowMax(tmp)
[1] 2.827186
> rowMin(tmp)
[1] -2.357118
>
> colMeans(tmp)
[1] 2.82718555 1.27040900 1.88211968 0.39828901 0.86124108 1.50502300
[7] 1.62955292 0.10740926 -0.38531201 -1.37862770 0.83022462 0.60078712
[13] -0.64620120 0.96140001 -1.44526533 1.00292942 0.60851362 -0.14938113
[19] -0.58354733 0.78795763 -0.18598657 0.66434767 -0.07195630 -0.03412151
[25] -0.88863477 1.17094664 1.11395328 -2.10132673 -1.78992930 -2.35711787
[31] -0.06756208 -0.65417069 0.32229212 -1.31611432 1.95170646 -0.22820816
[37] -0.91141412 -0.86185482 -0.20983107 -0.40284635 0.39484103 -0.06552329
[43] -0.95303401 -0.25085686 0.48841763 0.36232371 0.73622160 1.07584060
[49] 0.53668206 0.12721369 0.90117443 0.35075223 -1.38686874 0.83566390
[55] -0.15125987 0.47699696 -1.04098865 0.88428311 -1.42318392 1.22043012
[61] -0.76119362 -0.72892226 0.52643320 0.52993385 0.57638529 -0.33495103
[67] -0.31086822 1.17777643 -1.39108563 1.32122392 0.15223368 -0.10645217
[73] -0.27658620 1.19576329 0.17350904 0.64282526 0.02331125 -1.06807773
[79] 0.68322140 0.06258768 0.53063268 1.09595438 1.71932524 -0.06746108
[85] 0.86347456 1.59002410 -1.14388423 -0.63621590 0.91662356 -0.29004857
[91] -2.07250600 0.51542547 0.04429379 -1.52771062 0.51295458 -0.29190917
[97] 0.51233757 0.32318851 1.26292601 0.77660275
> colSums(tmp)
[1] 2.82718555 1.27040900 1.88211968 0.39828901 0.86124108 1.50502300
[7] 1.62955292 0.10740926 -0.38531201 -1.37862770 0.83022462 0.60078712
[13] -0.64620120 0.96140001 -1.44526533 1.00292942 0.60851362 -0.14938113
[19] -0.58354733 0.78795763 -0.18598657 0.66434767 -0.07195630 -0.03412151
[25] -0.88863477 1.17094664 1.11395328 -2.10132673 -1.78992930 -2.35711787
[31] -0.06756208 -0.65417069 0.32229212 -1.31611432 1.95170646 -0.22820816
[37] -0.91141412 -0.86185482 -0.20983107 -0.40284635 0.39484103 -0.06552329
[43] -0.95303401 -0.25085686 0.48841763 0.36232371 0.73622160 1.07584060
[49] 0.53668206 0.12721369 0.90117443 0.35075223 -1.38686874 0.83566390
[55] -0.15125987 0.47699696 -1.04098865 0.88428311 -1.42318392 1.22043012
[61] -0.76119362 -0.72892226 0.52643320 0.52993385 0.57638529 -0.33495103
[67] -0.31086822 1.17777643 -1.39108563 1.32122392 0.15223368 -0.10645217
[73] -0.27658620 1.19576329 0.17350904 0.64282526 0.02331125 -1.06807773
[79] 0.68322140 0.06258768 0.53063268 1.09595438 1.71932524 -0.06746108
[85] 0.86347456 1.59002410 -1.14388423 -0.63621590 0.91662356 -0.29004857
[91] -2.07250600 0.51542547 0.04429379 -1.52771062 0.51295458 -0.29190917
[97] 0.51233757 0.32318851 1.26292601 0.77660275
> colVars(tmp)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> colSd(tmp)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> colMax(tmp)
[1] 2.82718555 1.27040900 1.88211968 0.39828901 0.86124108 1.50502300
[7] 1.62955292 0.10740926 -0.38531201 -1.37862770 0.83022462 0.60078712
[13] -0.64620120 0.96140001 -1.44526533 1.00292942 0.60851362 -0.14938113
[19] -0.58354733 0.78795763 -0.18598657 0.66434767 -0.07195630 -0.03412151
[25] -0.88863477 1.17094664 1.11395328 -2.10132673 -1.78992930 -2.35711787
[31] -0.06756208 -0.65417069 0.32229212 -1.31611432 1.95170646 -0.22820816
[37] -0.91141412 -0.86185482 -0.20983107 -0.40284635 0.39484103 -0.06552329
[43] -0.95303401 -0.25085686 0.48841763 0.36232371 0.73622160 1.07584060
[49] 0.53668206 0.12721369 0.90117443 0.35075223 -1.38686874 0.83566390
[55] -0.15125987 0.47699696 -1.04098865 0.88428311 -1.42318392 1.22043012
[61] -0.76119362 -0.72892226 0.52643320 0.52993385 0.57638529 -0.33495103
[67] -0.31086822 1.17777643 -1.39108563 1.32122392 0.15223368 -0.10645217
[73] -0.27658620 1.19576329 0.17350904 0.64282526 0.02331125 -1.06807773
[79] 0.68322140 0.06258768 0.53063268 1.09595438 1.71932524 -0.06746108
[85] 0.86347456 1.59002410 -1.14388423 -0.63621590 0.91662356 -0.29004857
[91] -2.07250600 0.51542547 0.04429379 -1.52771062 0.51295458 -0.29190917
[97] 0.51233757 0.32318851 1.26292601 0.77660275
> colMin(tmp)
[1] 2.82718555 1.27040900 1.88211968 0.39828901 0.86124108 1.50502300
[7] 1.62955292 0.10740926 -0.38531201 -1.37862770 0.83022462 0.60078712
[13] -0.64620120 0.96140001 -1.44526533 1.00292942 0.60851362 -0.14938113
[19] -0.58354733 0.78795763 -0.18598657 0.66434767 -0.07195630 -0.03412151
[25] -0.88863477 1.17094664 1.11395328 -2.10132673 -1.78992930 -2.35711787
[31] -0.06756208 -0.65417069 0.32229212 -1.31611432 1.95170646 -0.22820816
[37] -0.91141412 -0.86185482 -0.20983107 -0.40284635 0.39484103 -0.06552329
[43] -0.95303401 -0.25085686 0.48841763 0.36232371 0.73622160 1.07584060
[49] 0.53668206 0.12721369 0.90117443 0.35075223 -1.38686874 0.83566390
[55] -0.15125987 0.47699696 -1.04098865 0.88428311 -1.42318392 1.22043012
[61] -0.76119362 -0.72892226 0.52643320 0.52993385 0.57638529 -0.33495103
[67] -0.31086822 1.17777643 -1.39108563 1.32122392 0.15223368 -0.10645217
[73] -0.27658620 1.19576329 0.17350904 0.64282526 0.02331125 -1.06807773
[79] 0.68322140 0.06258768 0.53063268 1.09595438 1.71932524 -0.06746108
[85] 0.86347456 1.59002410 -1.14388423 -0.63621590 0.91662356 -0.29004857
[91] -2.07250600 0.51542547 0.04429379 -1.52771062 0.51295458 -0.29190917
[97] 0.51233757 0.32318851 1.26292601 0.77660275
> colMedians(tmp)
[1] 2.82718555 1.27040900 1.88211968 0.39828901 0.86124108 1.50502300
[7] 1.62955292 0.10740926 -0.38531201 -1.37862770 0.83022462 0.60078712
[13] -0.64620120 0.96140001 -1.44526533 1.00292942 0.60851362 -0.14938113
[19] -0.58354733 0.78795763 -0.18598657 0.66434767 -0.07195630 -0.03412151
[25] -0.88863477 1.17094664 1.11395328 -2.10132673 -1.78992930 -2.35711787
[31] -0.06756208 -0.65417069 0.32229212 -1.31611432 1.95170646 -0.22820816
[37] -0.91141412 -0.86185482 -0.20983107 -0.40284635 0.39484103 -0.06552329
[43] -0.95303401 -0.25085686 0.48841763 0.36232371 0.73622160 1.07584060
[49] 0.53668206 0.12721369 0.90117443 0.35075223 -1.38686874 0.83566390
[55] -0.15125987 0.47699696 -1.04098865 0.88428311 -1.42318392 1.22043012
[61] -0.76119362 -0.72892226 0.52643320 0.52993385 0.57638529 -0.33495103
[67] -0.31086822 1.17777643 -1.39108563 1.32122392 0.15223368 -0.10645217
[73] -0.27658620 1.19576329 0.17350904 0.64282526 0.02331125 -1.06807773
[79] 0.68322140 0.06258768 0.53063268 1.09595438 1.71932524 -0.06746108
[85] 0.86347456 1.59002410 -1.14388423 -0.63621590 0.91662356 -0.29004857
[91] -2.07250600 0.51542547 0.04429379 -1.52771062 0.51295458 -0.29190917
[97] 0.51233757 0.32318851 1.26292601 0.77660275
> colRanges(tmp)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 2.827186 1.270409 1.88212 0.398289 0.8612411 1.505023 1.629553 0.1074093
[2,] 2.827186 1.270409 1.88212 0.398289 0.8612411 1.505023 1.629553 0.1074093
[,9] [,10] [,11] [,12] [,13] [,14] [,15]
[1,] -0.385312 -1.378628 0.8302246 0.6007871 -0.6462012 0.9614 -1.445265
[2,] -0.385312 -1.378628 0.8302246 0.6007871 -0.6462012 0.9614 -1.445265
[,16] [,17] [,18] [,19] [,20] [,21] [,22]
[1,] 1.002929 0.6085136 -0.1493811 -0.5835473 0.7879576 -0.1859866 0.6643477
[2,] 1.002929 0.6085136 -0.1493811 -0.5835473 0.7879576 -0.1859866 0.6643477
[,23] [,24] [,25] [,26] [,27] [,28] [,29]
[1,] -0.0719563 -0.03412151 -0.8886348 1.170947 1.113953 -2.101327 -1.789929
[2,] -0.0719563 -0.03412151 -0.8886348 1.170947 1.113953 -2.101327 -1.789929
[,30] [,31] [,32] [,33] [,34] [,35] [,36]
[1,] -2.357118 -0.06756208 -0.6541707 0.3222921 -1.316114 1.951706 -0.2282082
[2,] -2.357118 -0.06756208 -0.6541707 0.3222921 -1.316114 1.951706 -0.2282082
[,37] [,38] [,39] [,40] [,41] [,42] [,43]
[1,] -0.9114141 -0.8618548 -0.2098311 -0.4028464 0.394841 -0.06552329 -0.953034
[2,] -0.9114141 -0.8618548 -0.2098311 -0.4028464 0.394841 -0.06552329 -0.953034
[,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] -0.2508569 0.4884176 0.3623237 0.7362216 1.075841 0.5366821 0.1272137
[2,] -0.2508569 0.4884176 0.3623237 0.7362216 1.075841 0.5366821 0.1272137
[,51] [,52] [,53] [,54] [,55] [,56] [,57]
[1,] 0.9011744 0.3507522 -1.386869 0.8356639 -0.1512599 0.476997 -1.040989
[2,] 0.9011744 0.3507522 -1.386869 0.8356639 -0.1512599 0.476997 -1.040989
[,58] [,59] [,60] [,61] [,62] [,63] [,64]
[1,] 0.8842831 -1.423184 1.22043 -0.7611936 -0.7289223 0.5264332 0.5299339
[2,] 0.8842831 -1.423184 1.22043 -0.7611936 -0.7289223 0.5264332 0.5299339
[,65] [,66] [,67] [,68] [,69] [,70] [,71]
[1,] 0.5763853 -0.334951 -0.3108682 1.177776 -1.391086 1.321224 0.1522337
[2,] 0.5763853 -0.334951 -0.3108682 1.177776 -1.391086 1.321224 0.1522337
[,72] [,73] [,74] [,75] [,76] [,77] [,78]
[1,] -0.1064522 -0.2765862 1.195763 0.173509 0.6428253 0.02331125 -1.068078
[2,] -0.1064522 -0.2765862 1.195763 0.173509 0.6428253 0.02331125 -1.068078
[,79] [,80] [,81] [,82] [,83] [,84] [,85]
[1,] 0.6832214 0.06258768 0.5306327 1.095954 1.719325 -0.06746108 0.8634746
[2,] 0.6832214 0.06258768 0.5306327 1.095954 1.719325 -0.06746108 0.8634746
[,86] [,87] [,88] [,89] [,90] [,91] [,92]
[1,] 1.590024 -1.143884 -0.6362159 0.9166236 -0.2900486 -2.072506 0.5154255
[2,] 1.590024 -1.143884 -0.6362159 0.9166236 -0.2900486 -2.072506 0.5154255
[,93] [,94] [,95] [,96] [,97] [,98] [,99]
[1,] 0.04429379 -1.527711 0.5129546 -0.2919092 0.5123376 0.3231885 1.262926
[2,] 0.04429379 -1.527711 0.5129546 -0.2919092 0.5123376 0.3231885 1.262926
[,100]
[1,] 0.7766028
[2,] 0.7766028
>
>
> Max(tmp2)
[1] 2.692151
> Min(tmp2)
[1] -2.282721
> mean(tmp2)
[1] 0.04954917
> Sum(tmp2)
[1] 4.954917
> Var(tmp2)
[1] 1.1243
>
> rowMeans(tmp2)
[1] -0.167946254 -0.607861882 -0.650095623 1.332853543 0.223905420
[6] 0.723980988 -1.817786512 -0.768125795 0.425863712 -0.848862283
[11] -2.282721097 1.126623511 -0.794505949 -1.299631129 1.529343928
[16] -0.536244433 0.924424786 -1.226666719 1.509772119 1.455014681
[21] 0.070198622 0.253089378 -0.885010559 -1.385196977 -0.708297085
[26] -0.838095213 0.903391748 -0.540080421 0.196758745 0.003129761
[31] 1.601084882 0.214623490 0.978243265 0.066988362 0.112752919
[36] -1.348384291 0.281540524 -0.310230295 -0.269262393 0.546982674
[41] 0.801918351 -1.123507327 -2.258961779 1.791893141 -0.696654172
[46] -0.581546104 0.484623239 -0.002508202 -1.011365014 0.956789408
[51] -0.232230970 -0.525049049 1.174046608 0.538904195 1.395127182
[56] 0.047586032 -0.047695571 0.237802524 -0.911456775 -2.208642803
[61] -0.708571540 -0.780586483 0.610393281 -0.141392691 1.752966766
[66] 0.839998478 -0.540890084 0.687149911 -0.445295219 0.174186239
[71] 0.720365167 1.377156058 -1.586451598 -0.536442433 -0.908953178
[76] -0.889871404 -1.266093928 -0.163740018 1.018358812 1.620099891
[81] 1.560415991 0.175993836 0.759649998 1.197496350 2.448314038
[86] -1.120404527 0.102229533 2.219492051 2.692150539 -0.824755484
[91] -0.583525941 0.282737097 -1.751666142 0.871167790 0.804258287
[96] -0.360824891 0.487491609 1.112583671 0.357544430 -1.332452011
> rowSums(tmp2)
[1] -0.167946254 -0.607861882 -0.650095623 1.332853543 0.223905420
[6] 0.723980988 -1.817786512 -0.768125795 0.425863712 -0.848862283
[11] -2.282721097 1.126623511 -0.794505949 -1.299631129 1.529343928
[16] -0.536244433 0.924424786 -1.226666719 1.509772119 1.455014681
[21] 0.070198622 0.253089378 -0.885010559 -1.385196977 -0.708297085
[26] -0.838095213 0.903391748 -0.540080421 0.196758745 0.003129761
[31] 1.601084882 0.214623490 0.978243265 0.066988362 0.112752919
[36] -1.348384291 0.281540524 -0.310230295 -0.269262393 0.546982674
[41] 0.801918351 -1.123507327 -2.258961779 1.791893141 -0.696654172
[46] -0.581546104 0.484623239 -0.002508202 -1.011365014 0.956789408
[51] -0.232230970 -0.525049049 1.174046608 0.538904195 1.395127182
[56] 0.047586032 -0.047695571 0.237802524 -0.911456775 -2.208642803
[61] -0.708571540 -0.780586483 0.610393281 -0.141392691 1.752966766
[66] 0.839998478 -0.540890084 0.687149911 -0.445295219 0.174186239
[71] 0.720365167 1.377156058 -1.586451598 -0.536442433 -0.908953178
[76] -0.889871404 -1.266093928 -0.163740018 1.018358812 1.620099891
[81] 1.560415991 0.175993836 0.759649998 1.197496350 2.448314038
[86] -1.120404527 0.102229533 2.219492051 2.692150539 -0.824755484
[91] -0.583525941 0.282737097 -1.751666142 0.871167790 0.804258287
[96] -0.360824891 0.487491609 1.112583671 0.357544430 -1.332452011
> rowVars(tmp2)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> rowSd(tmp2)
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
> rowMax(tmp2)
[1] -0.167946254 -0.607861882 -0.650095623 1.332853543 0.223905420
[6] 0.723980988 -1.817786512 -0.768125795 0.425863712 -0.848862283
[11] -2.282721097 1.126623511 -0.794505949 -1.299631129 1.529343928
[16] -0.536244433 0.924424786 -1.226666719 1.509772119 1.455014681
[21] 0.070198622 0.253089378 -0.885010559 -1.385196977 -0.708297085
[26] -0.838095213 0.903391748 -0.540080421 0.196758745 0.003129761
[31] 1.601084882 0.214623490 0.978243265 0.066988362 0.112752919
[36] -1.348384291 0.281540524 -0.310230295 -0.269262393 0.546982674
[41] 0.801918351 -1.123507327 -2.258961779 1.791893141 -0.696654172
[46] -0.581546104 0.484623239 -0.002508202 -1.011365014 0.956789408
[51] -0.232230970 -0.525049049 1.174046608 0.538904195 1.395127182
[56] 0.047586032 -0.047695571 0.237802524 -0.911456775 -2.208642803
[61] -0.708571540 -0.780586483 0.610393281 -0.141392691 1.752966766
[66] 0.839998478 -0.540890084 0.687149911 -0.445295219 0.174186239
[71] 0.720365167 1.377156058 -1.586451598 -0.536442433 -0.908953178
[76] -0.889871404 -1.266093928 -0.163740018 1.018358812 1.620099891
[81] 1.560415991 0.175993836 0.759649998 1.197496350 2.448314038
[86] -1.120404527 0.102229533 2.219492051 2.692150539 -0.824755484
[91] -0.583525941 0.282737097 -1.751666142 0.871167790 0.804258287
[96] -0.360824891 0.487491609 1.112583671 0.357544430 -1.332452011
> rowMin(tmp2)
[1] -0.167946254 -0.607861882 -0.650095623 1.332853543 0.223905420
[6] 0.723980988 -1.817786512 -0.768125795 0.425863712 -0.848862283
[11] -2.282721097 1.126623511 -0.794505949 -1.299631129 1.529343928
[16] -0.536244433 0.924424786 -1.226666719 1.509772119 1.455014681
[21] 0.070198622 0.253089378 -0.885010559 -1.385196977 -0.708297085
[26] -0.838095213 0.903391748 -0.540080421 0.196758745 0.003129761
[31] 1.601084882 0.214623490 0.978243265 0.066988362 0.112752919
[36] -1.348384291 0.281540524 -0.310230295 -0.269262393 0.546982674
[41] 0.801918351 -1.123507327 -2.258961779 1.791893141 -0.696654172
[46] -0.581546104 0.484623239 -0.002508202 -1.011365014 0.956789408
[51] -0.232230970 -0.525049049 1.174046608 0.538904195 1.395127182
[56] 0.047586032 -0.047695571 0.237802524 -0.911456775 -2.208642803
[61] -0.708571540 -0.780586483 0.610393281 -0.141392691 1.752966766
[66] 0.839998478 -0.540890084 0.687149911 -0.445295219 0.174186239
[71] 0.720365167 1.377156058 -1.586451598 -0.536442433 -0.908953178
[76] -0.889871404 -1.266093928 -0.163740018 1.018358812 1.620099891
[81] 1.560415991 0.175993836 0.759649998 1.197496350 2.448314038
[86] -1.120404527 0.102229533 2.219492051 2.692150539 -0.824755484
[91] -0.583525941 0.282737097 -1.751666142 0.871167790 0.804258287
[96] -0.360824891 0.487491609 1.112583671 0.357544430 -1.332452011
>
> colMeans(tmp2)
[1] 0.04954917
> colSums(tmp2)
[1] 4.954917
> colVars(tmp2)
[1] 1.1243
> colSd(tmp2)
[1] 1.06033
> colMax(tmp2)
[1] 2.692151
> colMin(tmp2)
[1] -2.282721
> colMedians(tmp2)
[1] 0.06859349
> colRanges(tmp2)
[,1]
[1,] -2.282721
[2,] 2.692151
>
> dataset1 <- matrix(dataset1,1,100)
>
> agree.checks(tmp,dataset1)
>
> dataset2 <- matrix(dataset2,100,1)
> agree.checks(tmp2,dataset2)
>
>
> tmp <- createBufferedMatrix(10,10)
>
> tmp[1:10,1:10] <- rnorm(100)
> colApply(tmp,sum)
[1] 1.1788459 -2.0188108 -2.1428946 1.7172024 0.5959688 0.2086925
[7] 1.0630325 -1.2674020 2.6976137 -3.4651675
> colApply(tmp,quantile)[,1]
[,1]
[1,] -1.20596937
[2,] -0.19367707
[3,] -0.02758808
[4,] 0.58454182
[5,] 1.73486350
>
> rowApply(tmp,sum)
[1] 1.8216114 -4.2631865 -2.1755002 -1.3078964 1.0199025 3.6115286
[7] 0.8343369 -0.2897915 1.4898460 -2.1737699
> rowApply(tmp,rank)[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 5 7 6 5 7 2 9 10 6 3
[2,] 7 3 3 2 8 10 7 5 1 1
[3,] 6 2 5 10 1 7 4 9 2 4
[4,] 4 4 4 7 4 4 5 8 10 9
[5,] 1 5 8 3 6 9 6 2 9 5
[6,] 2 8 2 8 10 6 3 6 3 10
[7,] 8 6 1 6 3 8 10 7 8 6
[8,] 3 10 10 1 9 3 1 3 4 2
[9,] 9 9 7 9 2 5 8 4 7 7
[10,] 10 1 9 4 5 1 2 1 5 8
>
> tmp <- createBufferedMatrix(5,20)
>
> tmp[1:5,1:20] <- rnorm(100)
> colApply(tmp,sum)
[1] 1.22318460 3.73683711 -1.89293078 0.55880223 -3.40499175 3.34547291
[7] -0.62331329 1.94860056 -2.77937491 0.06110267 -4.49963792 -1.11874128
[13] -0.80047182 -1.86368437 -0.46870034 2.73288768 0.32875879 5.24219532
[19] -0.28541074 1.00433688
> colApply(tmp,quantile)[,1]
[,1]
[1,] -0.36702971
[2,] -0.35002877
[3,] -0.01462807
[4,] 0.78546989
[5,] 1.16940127
>
> rowApply(tmp,sum)
[1] 5.516110 2.942375 -6.417661 2.419278 -2.015180
> rowApply(tmp,rank)[1:5,]
[,1] [,2] [,3] [,4] [,5]
[1,] 19 9 9 17 6
[2,] 8 19 10 20 14
[3,] 5 10 8 13 5
[4,] 18 11 7 8 15
[5,] 3 4 16 6 3
>
>
> as.matrix(tmp)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1.16940127 0.1413947 -0.822519619 1.11526053 -0.9936270 -0.01043150
[2,] -0.01462807 1.7498747 0.006908635 0.08947584 -0.7995779 0.96903319
[3,] -0.36702971 -0.3438934 -0.440031876 -0.79975751 0.3325083 0.89567598
[4,] 0.78546989 1.9087074 0.279810673 -0.13724384 -0.5074750 1.56447575
[5,] -0.35002877 0.2807538 -0.917098593 0.29106719 -1.4368202 -0.07328051
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0.6227639 0.9654841 -1.2029129 0.6827448 0.7996918 -0.9845854
[2,] 0.8987973 -1.0631109 0.1038166 0.9179927 -1.6872944 -0.2880081
[3,] -0.1130186 1.4049640 -1.5737703 -0.2018916 -0.2287182 -0.8373496
[4,] -0.4378567 -0.1020651 0.1950203 -1.0054690 -1.5864706 0.7381401
[5,] -1.5939991 0.7433285 -0.3015286 -0.3322742 -1.7968465 0.2530617
[,13] [,14] [,15] [,16] [,17] [,18]
[1,] -0.07328942 0.3417243 -1.1250369 2.23614658 0.9680490 0.9080009
[2,] 1.58618726 -0.3584623 -0.0506278 -0.62131771 -1.9429432 2.2747894
[3,] -0.29722900 -2.3806100 1.5917823 0.39561342 -1.1696172 0.3296679
[4,] -0.69244143 -0.6105528 -0.6236188 0.70071723 0.2392118 0.7653885
[5,] -1.32369923 1.1442164 -0.2611992 0.02172816 2.2340584 0.9643486
[,19] [,20]
[1,] 0.48056319 0.2972872
[2,] 0.80108224 0.3703870
[3,] -1.55301378 -1.0619416
[4,] 0.02583979 0.9196893
[5,] -0.03988219 0.4789150
>
>
> is.BufferedMatrix(tmp)
[1] TRUE
>
> as.BufferedMatrix(as.matrix(tmp))
BufferedMatrix object
Matrix size: 5 20
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 800 bytes.
>
>
>
> subBufferedMatrix(tmp,1:5,1:5)
BufferedMatrix object
Matrix size: 5 5
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 655 bytes.
Disk usage : 200 bytes.
> subBufferedMatrix(tmp,,5:8)
BufferedMatrix object
Matrix size: 5 4
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 567 bytes.
Disk usage : 160 bytes.
> subBufferedMatrix(tmp,1:3,)
BufferedMatrix object
Matrix size: 3 20
Buffer size: 1 1
Directory: /Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 1.9 Kilobytes.
Disk usage : 480 bytes.
>
>
> rm(tmp)
>
>
> ###
> ### Testing colnames and rownames
> ###
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
>
>
> colnames(tmp)
NULL
> rownames(tmp)
NULL
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> colnames(tmp)
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
> rownames(tmp)
[1] "row1" "row2" "row3" "row4" "row5"
>
>
> tmp["row1",]
col1 col2 col3 col4 col5 col6 col7
row1 0.3137895 -0.1799106 0.5620171 -1.146437 2.441176 1.464525 1.048245
col8 col9 col10 col11 col12 col13 col14
row1 0.2743024 1.307087 -1.103808 -0.7155773 0.2646721 -0.559405 -0.5112706
col15 col16 col17 col18 col19 col20
row1 -0.3502727 -1.311953 1.210574 -0.7598995 0.3814082 -0.7017177
> tmp[,"col10"]
col10
row1 -1.1038081
row2 0.9452084
row3 -1.5296705
row4 1.0714378
row5 0.2859111
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6 col7
row1 0.31378954 -0.17991061 0.5620171 -1.1464365 2.4411762 1.4645245 1.048245
row5 0.03869596 0.05740298 -0.3154679 -0.2377427 -0.9650306 0.3150751 1.233210
col8 col9 col10 col11 col12 col13 col14
row1 0.2743024 1.307087 -1.1038081 -0.7155773 0.2646721 -0.5594050 -0.5112706
row5 -1.1188265 -1.357422 0.2859111 -2.5023972 0.2308857 0.8148787 -0.1524977
col15 col16 col17 col18 col19 col20
row1 -0.3502727 -1.3119526 1.210574 -0.7598995 0.3814082 -0.7017177
row5 -1.6646284 0.4678648 -0.976009 1.1139458 -1.3378712 -1.0159585
> tmp[,c("col6","col20")]
col6 col20
row1 1.4645245 -0.7017177
row2 -1.3568679 -0.4774498
row3 1.8371817 -0.6525231
row4 0.8041342 1.1313574
row5 0.3150751 -1.0159585
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 1.4645245 -0.7017177
row5 0.3150751 -1.0159585
>
>
>
>
> tmp["row1",] <- rnorm(20,mean=10)
> tmp[,"col10"] <- rnorm(5,mean=30)
> tmp[c("row1","row5"),] <- rnorm(40,mean=50)
> tmp[,c("col6","col20")] <- rnorm(10,mean=75)
> tmp[c("row1","row5"),c("col6","col20")] <- rnorm(4,mean=105)
>
> tmp["row1",]
col1 col2 col3 col4 col5 col6 col7 col8
row1 50.04791 48.78271 49.54665 48.80563 50.18728 108.1252 52.10065 48.45092
col9 col10 col11 col12 col13 col14 col15 col16
row1 49.87015 49.95582 50.75375 50.86413 50.20698 47.38499 50.78531 51.40446
col17 col18 col19 col20
row1 50.76836 51.41518 49.90183 106.7408
> tmp[,"col10"]
col10
row1 49.95582
row2 29.60549
row3 28.21563
row4 31.00114
row5 50.81309
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6 col7 col8
row1 50.04791 48.78271 49.54665 48.80563 50.18728 108.1252 52.10065 48.45092
row5 50.21187 49.09714 49.93566 49.17040 51.87914 103.7458 50.86454 49.88674
col9 col10 col11 col12 col13 col14 col15 col16
row1 49.87015 49.95582 50.75375 50.86413 50.20698 47.38499 50.78531 51.40446
row5 49.86040 50.81309 49.11939 49.93913 50.54631 49.87980 49.99890 48.58670
col17 col18 col19 col20
row1 50.76836 51.41518 49.90183 106.7408
row5 49.35984 49.62624 50.09728 105.7936
> tmp[,c("col6","col20")]
col6 col20
row1 108.12521 106.74083
row2 74.72164 73.15750
row3 74.52592 76.26914
row4 74.90780 74.42792
row5 103.74581 105.79356
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 108.1252 106.7408
row5 103.7458 105.7936
>
>
> subBufferedMatrix(tmp,c("row1","row5"),c("col6","col20"))[1:2,1:2]
col6 col20
row1 108.1252 106.7408
row5 103.7458 105.7936
>
>
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
>
> tmp[,"col13"]
col13
[1,] 0.05877325
[2,] -0.69455515
[3,] 2.60994213
[4,] -0.17952285
[5,] -2.32715713
> tmp[,c("col17","col7")]
col17 col7
[1,] -0.564630566 -0.5125154
[2,] 0.981521133 -1.2924714
[3,] 0.054881886 -1.1773994
[4,] -0.004553985 -0.7008954
[5,] -1.596750755 0.4161558
>
> subBufferedMatrix(tmp,,c("col6","col20"))[,1:2]
col6 col20
[1,] -0.5149623 -0.7615255
[2,] 1.1352118 -0.7576212
[3,] 1.7362882 -2.5936161
[4,] -1.1318500 -0.9698923
[5,] -0.7304188 -0.2122200
> subBufferedMatrix(tmp,1,c("col6"))[,1]
col1
[1,] -0.5149623
> subBufferedMatrix(tmp,1:2,c("col6"))[,1]
col6
[1,] -0.5149623
[2,] 1.1352118
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
>
>
>
> subBufferedMatrix(tmp,c("row3","row1"),)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row3 1.082667 -0.7046937 0.4626562 -0.1498166 -0.7162804 -0.3699512 -1.653896
row1 -1.083336 -0.4279523 -2.1625312 -0.3003785 -0.9016100 -0.9909458 -1.006320
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
row3 1.058236 0.4980295 -0.3556008 -0.02931597 0.07310573 -1.0943569 0.1068327
row1 2.238130 0.2822979 -0.0393385 -0.78807645 0.01266957 -0.4102347 -0.8396964
[,15] [,16] [,17] [,18] [,19] [,20]
row3 -0.26856491 2.145679 0.01605845 -0.46100886 -0.03496212 -0.6902886
row1 -0.06601736 -0.189780 -0.17292759 0.07905042 -0.44253842 3.4799588
> subBufferedMatrix(tmp,c("row2"),1:10)[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row2 0.9656725 -1.246251 0.7906154 0.8701598 0.3015772 1.01034 0.8589667
[,8] [,9] [,10]
row2 1.503509 1.128086 2.063091
> subBufferedMatrix(tmp,c("row5"),1:20)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row5 -1.968632 -0.225809 0.8960463 -0.1620018 -0.0123754 0.3309203 0.5400627
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
row5 0.4321913 0.2716559 1.574793 0.09010511 0.6667638 -1.06533 0.8022857
[,15] [,16] [,17] [,18] [,19] [,20]
row5 -0.1896641 -0.1678318 -0.7503156 1.94965 -0.004175823 -1.013392
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> colnames(tmp)
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
> rownames(tmp)
[1] "row1" "row2" "row3" "row4" "row5"
>
>
> colnames(tmp) <- NULL
> rownames(tmp) <- NULL
>
> colnames(tmp)
NULL
> rownames(tmp)
NULL
>
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
>
> dimnames(tmp)
[[1]]
[1] "row1" "row2" "row3" "row4" "row5"
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
> dimnames(tmp) <- NULL
>
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
> dimnames(tmp)
[[1]]
NULL
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
>
> dimnames(tmp) <- NULL
> rownames(tmp) <- rownames(tmp,do.NULL=FALSE)
> dimnames(tmp)
[[1]]
[1] "row1" "row2" "row3" "row4" "row5"
[[2]]
NULL
>
> dimnames(tmp) <- list(NULL,c(colnames(tmp,do.NULL=FALSE)))
> dimnames(tmp)
[[1]]
NULL
[[2]]
[1] "col1" "col2" "col3" "col4" "col5" "col6" "col7" "col8" "col9"
[10] "col10" "col11" "col12" "col13" "col14" "col15" "col16" "col17" "col18"
[19] "col19" "col20"
>
>
>
> ###
> ### Testing logical indexing
> ###
> ###
>
> tmp <- createBufferedMatrix(230,15)
> tmp[1:230,1:15] <- rnorm(230*15)
> x <-tmp[1:230,1:15]
>
> for (rep in 1:10){
+ which.cols <- sample(c(TRUE,FALSE),15,replace=T)
+ which.rows <- sample(c(TRUE,FALSE),230,replace=T)
+
+ if (!all(tmp[which.rows,which.cols] == x[which.rows,which.cols])){
+ stop("No agreement when logical indexing\n")
+ }
+
+ if (!all(subBufferedMatrix(tmp,,which.cols)[,1:sum(which.cols)] == x[,which.cols])){
+ stop("No agreement when logical indexing in subBufferedMatrix cols\n")
+ }
+ if (!all(subBufferedMatrix(tmp,which.rows,)[1:sum(which.rows),] == x[which.rows,])){
+ stop("No agreement when logical indexing in subBufferedMatrix rows\n")
+ }
+
+
+ if (!all(subBufferedMatrix(tmp,which.rows,which.cols)[1:sum(which.rows),1:sum(which.cols)]== x[which.rows,which.cols])){
+ stop("No agreement when logical indexing in subBufferedMatrix rows and columns\n")
+ }
+ }
>
>
> ##
> ## Test the ReadOnlyMode
> ##
>
> ReadOnlyMode(tmp)
<pointer: 0x600000f244e0>
> is.ReadOnlyMode(tmp)
[1] TRUE
>
> filenames(tmp)
[1] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a513a9e0"
[2] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a4df5b3ba"
[3] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a3deaa052"
[4] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a76c3873f"
[5] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a1ee47603"
[6] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a28ffcecd"
[7] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a326209c2"
[8] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a426eb965"
[9] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a734db7f5"
[10] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a736870f6"
[11] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a4dd061ab"
[12] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a2bbc4976"
[13] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a567af868"
[14] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a234aa033"
[15] "/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests/BM14b3a785b865e"
>
>
> ### testing coercion functions
> ###
>
> tmp <- as(tmp,"matrix")
> tmp <- as(tmp,"BufferedMatrix")
>
>
>
> ### testing whether can move storage from one location to another
>
> MoveStorageDirectory(tmp,"NewDirectory",full.path=FALSE)
<pointer: 0x600000f0c900>
> MoveStorageDirectory(tmp,getwd(),full.path=TRUE)
<pointer: 0x600000f0c900>
Warning message:
In dir.create(new.directory) :
'/Users/biocbuild/bbs-3.23-bioc/meat/BufferedMatrix.Rcheck/tests' already exists
>
>
> RowMode(tmp)
<pointer: 0x600000f0c900>
> rowMedians(tmp)
[1] -0.066989667 0.092548547 -0.143955472 0.064738895 -0.039448398
[6] -0.083753181 -0.606642027 0.664116395 -0.431245825 -0.326450340
[11] 0.399444787 1.037765419 0.158359919 0.008071458 0.249240395
[16] 0.140147851 0.119375394 -0.019727937 -0.856310042 0.234767496
[21] -0.093045211 0.447353454 -0.212795053 -0.021674775 -0.349615401
[26] -0.674204672 0.099166309 0.112228192 0.459310309 -0.085514344
[31] -0.124289780 -0.273919848 -0.448630746 -0.386090703 -0.447375888
[36] 0.031949886 0.144082473 -0.665711636 0.747640562 -0.446683090
[41] 0.272514270 0.178251212 0.355198634 -0.170581922 -0.186628394
[46] 0.270445163 -0.166721920 0.457282782 -0.184608377 -0.192447484
[51] 0.063393079 -0.128874044 -0.349659144 0.274342830 -0.040857834
[56] -0.676788781 0.168724106 -0.022909690 0.222611445 -0.310670826
[61] 0.513039316 -0.346756675 -0.021720273 -0.054744932 0.526702456
[66] -0.130233234 -0.038816459 0.083397116 0.424418486 0.152573899
[71] 0.290227405 0.184176288 0.147415583 -0.240043709 0.050625216
[76] -0.062377822 -0.253279324 -0.328011866 0.136199172 0.046517137
[81] -0.118439122 0.127136958 0.441629887 0.109702926 0.172505014
[86] 0.113751794 0.386643283 -0.059166881 0.348057033 -0.007111463
[91] -0.440709176 0.731674542 -0.289329759 0.121931175 0.168205137
[96] 0.301232806 -0.239132130 -0.003657551 0.023650331 0.419326947
[101] -0.449076754 0.042367385 0.114800011 0.119007905 0.001477415
[106] 0.042968787 -0.408345918 0.073815275 0.194838208 0.214169361
[111] -0.277889937 0.155711254 -0.008062706 -0.429837802 -0.515654704
[116] -0.030219940 -0.077613174 -0.077990000 -0.317437322 -0.127057671
[121] -0.442457530 0.049447388 0.405123782 -0.311327722 -0.167919686
[126] 0.045898299 -0.068700697 -0.097464678 0.107082292 -0.116038263
[131] -0.793545442 -0.212985504 -0.313214661 0.144984099 -0.374629027
[136] -0.156332171 0.378508438 -0.038330501 -0.332981671 0.289963420
[141] -0.100013968 0.533559212 0.281220421 0.014828085 0.466946870
[146] 0.424135635 -0.232954766 -0.079254755 0.508602651 0.149214070
[151] -0.068100462 -0.595572111 -0.233284969 0.100044724 0.242090649
[156] 0.098436259 -0.019582379 -0.085929417 0.270602489 -0.182563590
[161] -0.549936526 0.242071659 -0.104859768 -0.079146618 -0.389652052
[166] -0.132337936 0.468514631 0.186748176 0.162006307 0.149324826
[171] -0.189991499 0.260508163 -0.415794426 -0.452775928 -0.040323052
[176] 0.349907833 0.046835805 -0.004745203 -0.395008125 -0.264425485
[181] -0.133581455 0.117230083 -0.128142688 0.091377536 0.448574798
[186] 0.237504858 0.077110623 0.226358509 -0.315900217 0.081551590
[191] 0.095556522 0.436707282 -0.352121189 0.266139245 0.373653748
[196] 0.300519951 -0.109183995 0.194723859 0.627115234 0.296680018
[201] -0.087864909 -0.028314322 -0.318047707 -0.011926333 0.692871063
[206] -0.374799708 0.167660283 -0.228747762 -0.396936973 0.138457564
[211] -0.387388483 0.004232804 0.071133222 0.166055846 -0.336600921
[216] 0.303410241 -0.071687276 -0.024785503 0.119034109 0.075881059
[221] -0.686780357 -0.292983177 -0.410601039 0.379434831 -0.560924154
[226] -0.379575744 -0.050321292 0.451099094 0.488762001 -0.158948772
>
> proc.time()
user system elapsed
0.750 4.242 5.452
BufferedMatrix.Rcheck/tests/rawCalltesting.Rout
R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
> prefix <- "dbmtest"
> directory <- getwd()
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x600001780000>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x600001780000>
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 10
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x600001780000>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 10
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1.000000 2.000000 3.000000 4.000000 5.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3.000000 4.000000 5.000000 6.000000 7.000000 0.000000 0.000000 0.000000 0.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 0.000000 0.000000 0.000000 0.000000 0.000000
<pointer: 0x600001780000>
> rm(P)
>
> #P <- .Call("R_bm_Destroy",P)
> #.Call("R_bm_Destroy",P)
> #.Call("R_bm_Test_C",P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,5)
[1] TRUE
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 0
Buffer Rows: 1
Buffer Cols: 1
Printing Values
<pointer: 0x600001788000>
> .Call("R_bm_AddColumn",P)
<pointer: 0x600001788000>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 1
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000
0.000000
0.000000
0.000000
0.000000
<pointer: 0x600001788000>
> .Call("R_bm_AddColumn",P)
<pointer: 0x600001788000>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x600001788000>
> rm(P)
>
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,5)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x6000017907e0>
> .Call("R_bm_AddColumn",P)
<pointer: 0x6000017907e0>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x6000017907e0>
>
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x6000017907e0>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x6000017907e0>
>
> .Call("R_bm_RowMode",P)
<pointer: 0x6000017907e0>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x6000017907e0>
>
> .Call("R_bm_ColMode",P)
<pointer: 0x6000017907e0>
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 2
Buffer Rows: 5
Buffer Cols: 5
Printing Values
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
<pointer: 0x6000017907e0>
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x6000017909c0>
> .Call("R_bm_SetPrefix",P,"BufferedMatrixFile")
<pointer: 0x6000017909c0>
> .Call("R_bm_AddColumn",P)
<pointer: 0x6000017909c0>
> .Call("R_bm_AddColumn",P)
<pointer: 0x6000017909c0>
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFile1507c1cf07e5e" "BufferedMatrixFile1507c4fb3ac3b"
> rm(P)
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFile1507c1cf07e5e" "BufferedMatrixFile1507c4fb3ac3b"
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x600001790c60>
> .Call("R_bm_AddColumn",P)
<pointer: 0x600001790c60>
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x600001790c60>
> .Call("R_bm_isReadOnlyMode",P)
[1] TRUE
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x600001790c60>
> .Call("R_bm_isReadOnlyMode",P)
[1] FALSE
> .Call("R_bm_isRowMode",P)
[1] FALSE
> .Call("R_bm_RowMode",P)
<pointer: 0x600001790c60>
> .Call("R_bm_isRowMode",P)
[1] TRUE
> .Call("R_bm_ColMode",P)
<pointer: 0x600001790c60>
> .Call("R_bm_isRowMode",P)
[1] FALSE
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x60000178c000>
> .Call("R_bm_AddColumn",P)
<pointer: 0x60000178c000>
>
> .Call("R_bm_getSize",P)
[1] 10 2
> .Call("R_bm_getBufferSize",P)
[1] 1 1
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x60000178c000>
>
> .Call("R_bm_getBufferSize",P)
[1] 5 5
> .Call("R_bm_ResizeBuffer",P,-1,5)
<pointer: 0x60000178c000>
> rm(P)
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_Test_C",P)
RBufferedMatrix
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Assigning Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 6.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x6000017845a0>
> .Call("R_bm_getValue",P,3,3)
[1] 6
>
> .Call("R_bm_getValue",P,100000,10000)
[1] NA
> .Call("R_bm_setValue",P,3,3,12345.0)
[1] TRUE
> .Call("R_bm_Test_C2",P)
Checking dimensions
Rows: 5
Cols: 5
Buffer Rows: 1
Buffer Cols: 1
Printing Values
0.000000 1.000000 2.000000 3.000000 4.000000
1.000000 2.000000 3.000000 4.000000 5.000000
2.000000 3.000000 4.000000 5.000000 6.000000
3.000000 4.000000 5.000000 12345.000000 7.000000
4.000000 5.000000 6.000000 7.000000 8.000000
<pointer: 0x6000017845a0>
> rm(P)
>
> proc.time()
user system elapsed
0.133 0.054 0.187
BufferedMatrix.Rcheck/tests/Rcodetesting.Rout
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Platform: aarch64-apple-darwin20
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> library(BufferedMatrix);library.dynam("BufferedMatrix","BufferedMatrix", .libPaths());
Attaching package: 'BufferedMatrix'
The following objects are masked from 'package:base':
colMeans, colSums, rowMeans, rowSums
>
> Temp <- createBufferedMatrix(100)
> dim(Temp)
[1] 100 0
> buffer.dim(Temp)
[1] 1 1
>
>
> proc.time()
user system elapsed
0.139 0.036 0.171