| Back to Build/check report for BioC 3.18: simplified long |
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This page was generated on 2023-06-06 11:00:27 -0000 (Tue, 06 Jun 2023).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| kunpeng2 | Linux (openEuler 22.03 LTS-SP1) | aarch64 | 4.3.0 (2023-04-21) -- "Already Tomorrow" | 4366 |
| 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 | ||||
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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. Note: If "R CMD check" recently failed on the Linux builder over a missing dependency, add the missing dependency to "Suggests" in your DESCRIPTION file. See the Renviron.bioc for details. |
| Package 241/2199 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| BufferedMatrix 1.65.0 (landing page) Ben Bolstad
| kunpeng2 | Linux (openEuler 22.03 LTS-SP1) / aarch64 | OK | OK | OK | |||||||||
| Package: BufferedMatrix |
| Version: 1.65.0 |
| Command: /home/biocbuild/R/R-4.3.0/bin/R CMD check --install=check:BufferedMatrix.install-out.txt --library=/home/biocbuild/R/R-4.3.0/site-library --timings BufferedMatrix_1.65.0.tar.gz |
| StartedAt: 2023-06-05 18:40:41 -0000 (Mon, 05 Jun 2023) |
| EndedAt: 2023-06-05 18:41:10 -0000 (Mon, 05 Jun 2023) |
| EllapsedTime: 29.0 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: BufferedMatrix.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /home/biocbuild/R/R-4.3.0/bin/R CMD check --install=check:BufferedMatrix.install-out.txt --library=/home/biocbuild/R/R-4.3.0/site-library --timings BufferedMatrix_1.65.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck’
* using R version 4.3.0 (2023-04-21)
* using platform: aarch64-unknown-linux-gnu (64-bit)
* R was compiled by
gcc (GCC) 10.3.1
GNU Fortran (GCC) 10.3.1
* running under: openEuler 22.03 (LTS-SP1)
* using session charset: UTF-8
* checking for file ‘BufferedMatrix/DESCRIPTION’ ... OK
* this is package ‘BufferedMatrix’ version ‘1.65.0’
* 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 ... OK
* used C compiler: ‘gcc (GCC) 10.3.1’
* 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 R 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 loading without being on the library search path ... 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
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 ... NOTE
Note: information on .o files is not available
* 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 in ‘inst/doc’ ... OK
* checking running R code from vignettes ...
‘BufferedMatrix.Rnw’... OK
OK
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
See
‘/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/00check.log’
for details.
BufferedMatrix.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/R/R-4.3.0/bin/R CMD INSTALL BufferedMatrix ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/R/R-4.3.0/site-library’ * installing *source* package ‘BufferedMatrix’ ... ** using staged installation ** libs using C compiler: ‘gcc (GCC) 10.3.1’ gcc -I"/home/biocbuild/R/R-4.3.0/include" -DNDEBUG -I/usr/local/include -fPIC -g -O2 -c RBufferedMatrix.c -o RBufferedMatrix.o gcc -I"/home/biocbuild/R/R-4.3.0/include" -DNDEBUG -I/usr/local/include -fPIC -g -O2 -c doubleBufferedMatrix.c -o doubleBufferedMatrix.o gcc -I"/home/biocbuild/R/R-4.3.0/include" -DNDEBUG -I/usr/local/include -fPIC -g -O2 -c doubleBufferedMatrix_C_tests.c -o doubleBufferedMatrix_C_tests.o gcc -I"/home/biocbuild/R/R-4.3.0/include" -DNDEBUG -I/usr/local/include -fPIC -g -O2 -c init_package.c -o init_package.o gcc -shared -L/home/biocbuild/R/R-4.3.0/lib -L/usr/local/lib -o BufferedMatrix.so RBufferedMatrix.o doubleBufferedMatrix.o doubleBufferedMatrix_C_tests.o init_package.o -L/home/biocbuild/R/R-4.3.0/lib -lR installing to /home/biocbuild/R/R-4.3.0/site-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 version 4.3.0 (2023-04-21) -- "Already Tomorrow"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu (64-bit)
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
0.320 0.044 0.352
BufferedMatrix.Rcheck/tests/objectTesting.Rout
R version 4.3.0 (2023-04-21) -- "Already Tomorrow"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu (64-bit)
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] "/home/biocbuild/bbs-3.18-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) max used (Mb)
Ncells 457526 24.5 981808 52.5 650817 34.8
Vcells 842770 6.5 8388608 64.0 2061571 15.8
>
>
>
>
> ##
> ## 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] "Mon Jun 5 18:41:01 2023"
> 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] "Mon Jun 5 18:41:02 2023"
>
>
> 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: 0x170ef770>
>
>
>
> 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] "Mon Jun 5 18:41:02 2023"
> 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] "Mon Jun 5 18:41:02 2023"
>
> ColMode(tmp2)
<pointer: 0x170ef770>
>
>
>
> ### 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,] 100.3155496 -0.5270337 1.1742369 0.49275890
[2,] 0.5849984 -1.3258040 -0.6484462 1.31064692
[3,] -1.5082038 -1.8756988 -1.7082707 0.81419418
[4,] 2.5529593 0.1041287 1.5154364 0.09352199
> ewApply(tmp5,abs)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: /home/biocbuild/bbs-3.18-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,] 100.3155496 0.5270337 1.1742369 0.49275890
[2,] 0.5849984 1.3258040 0.6484462 1.31064692
[3,] 1.5082038 1.8756988 1.7082707 0.81419418
[4,] 2.5529593 0.1041287 1.5154364 0.09352199
> ewApply(tmp5,sqrt)
BufferedMatrix object
Matrix size: 10 20
Buffer size: 1 1
Directory: /home/biocbuild/bbs-3.18-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,] 10.0157651 0.7259709 1.0836221 0.7019679
[2,] 0.7648519 1.1514356 0.8052616 1.1448349
[3,] 1.2280895 1.3695615 1.3070083 0.9023271
[4,] 1.5977983 0.3226898 1.2310306 0.3058136
>
> 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: /home/biocbuild/bbs-3.18-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,] 225.47320 32.78674 37.01046 32.51244
[2,] 33.23352 37.84016 33.70106 37.75900
[3,] 38.78910 40.57131 39.77835 34.83747
[4,] 43.53094 28.33103 38.82574 28.15166
>
>
>
> ## testing functions that elementwise transform the matrix
> sqrt(tmp5)
<pointer: 0x163cc380>
> exp(tmp5)
<pointer: 0x163cc380>
> log(tmp5,2)
<pointer: 0x163cc380>
> pow(tmp5,2)
>
>
>
>
>
> ## testing functions that apply to entire matrix
> Max(tmp5)
[1] 469.2929
> Min(tmp5)
[1] 52.45497
> mean(tmp5)
[1] 73.12123
> Sum(tmp5)
[1] 14624.25
> Var(tmp5)
[1] 865.8209
>
>
> ## testing functions applied to rows or columns
>
> rowMeans(tmp5)
[1] 89.37630 67.90427 73.12221 69.86892 71.68502 71.37263 70.79806 72.86449
[9] 73.55346 70.66695
> rowSums(tmp5)
[1] 1787.526 1358.085 1462.444 1397.378 1433.700 1427.453 1415.961 1457.290
[9] 1471.069 1413.339
> rowVars(tmp5)
[1] 8035.62907 57.27718 80.79652 83.65886 69.11297 85.57967
[7] 74.96087 77.58471 71.49638 96.64399
> rowSd(tmp5)
[1] 89.641670 7.568169 8.988688 9.146522 8.313421 9.250928 8.657995
[8] 8.808218 8.455553 9.830767
> rowMax(tmp5)
[1] 469.29292 78.75934 84.68889 90.60395 86.29349 88.92496 85.21875
[8] 87.19232 89.74008 93.12952
> rowMin(tmp5)
[1] 52.45497 53.59228 55.28424 56.38790 55.58273 58.82742 53.87534 53.13138
[9] 58.63558 57.60934
>
> colMeans(tmp5)
[1] 110.01952 73.44414 73.33788 71.79760 68.73292 75.38037 74.62225
[8] 67.86172 70.92570 69.38137 71.47834 66.43136 71.37040 72.34080
[15] 71.61602 71.50762 71.78473 71.60851 66.51805 72.26530
> colSums(tmp5)
[1] 1100.1952 734.4414 733.3788 717.9760 687.3292 753.8037 746.2225
[8] 678.6172 709.2570 693.8137 714.7834 664.3136 713.7040 723.4080
[15] 716.1602 715.0762 717.8473 716.0851 665.1805 722.6530
> colVars(tmp5)
[1] 16057.44922 93.92457 96.13626 43.91081 58.53987 127.44421
[7] 84.99041 72.82916 31.39833 114.50040 57.08606 34.94579
[13] 48.72697 76.75390 82.93259 36.93238 96.57059 81.33990
[19] 60.56267 75.20314
> colSd(tmp5)
[1] 126.717991 9.691469 9.804910 6.626523 7.651135 11.289119
[7] 9.219024 8.534000 5.603421 10.700486 7.555532 5.911497
[13] 6.980471 8.760930 9.106733 6.077202 9.827034 9.018864
[19] 7.782202 8.671974
> colMax(tmp5)
[1] 469.29292 89.12400 87.19232 81.25145 78.29301 93.12952 89.96853
[8] 79.09390 81.00748 83.83299 81.39862 74.77780 81.17246 85.04668
[15] 83.72521 79.19200 88.92496 86.29349 77.44704 81.82858
> colMin(tmp5)
[1] 53.13138 58.96732 57.60934 58.59399 56.38790 62.84909 61.07238 52.45497
[9] 64.82770 53.87534 60.34313 55.40136 61.05314 55.58273 58.46130 60.19625
[17] 59.05537 58.82742 53.59228 61.00961
>
>
> ### 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] 89.37630 67.90427 73.12221 NA 71.68502 71.37263 70.79806 72.86449
[9] 73.55346 70.66695
> rowSums(tmp5)
[1] 1787.526 1358.085 1462.444 NA 1433.700 1427.453 1415.961 1457.290
[9] 1471.069 1413.339
> rowVars(tmp5)
[1] 8035.62907 57.27718 80.79652 85.22327 69.11297 85.57967
[7] 74.96087 77.58471 71.49638 96.64399
> rowSd(tmp5)
[1] 89.641670 7.568169 8.988688 9.231645 8.313421 9.250928 8.657995
[8] 8.808218 8.455553 9.830767
> rowMax(tmp5)
[1] 469.29292 78.75934 84.68889 NA 86.29349 88.92496 85.21875
[8] 87.19232 89.74008 93.12952
> rowMin(tmp5)
[1] 52.45497 53.59228 55.28424 NA 55.58273 58.82742 53.87534 53.13138
[9] 58.63558 57.60934
>
> colMeans(tmp5)
[1] 110.01952 73.44414 73.33788 71.79760 68.73292 75.38037 74.62225
[8] 67.86172 70.92570 69.38137 71.47834 66.43136 71.37040 72.34080
[15] NA 71.50762 71.78473 71.60851 66.51805 72.26530
> colSums(tmp5)
[1] 1100.1952 734.4414 733.3788 717.9760 687.3292 753.8037 746.2225
[8] 678.6172 709.2570 693.8137 714.7834 664.3136 713.7040 723.4080
[15] NA 715.0762 717.8473 716.0851 665.1805 722.6530
> colVars(tmp5)
[1] 16057.44922 93.92457 96.13626 43.91081 58.53987 127.44421
[7] 84.99041 72.82916 31.39833 114.50040 57.08606 34.94579
[13] 48.72697 76.75390 NA 36.93238 96.57059 81.33990
[19] 60.56267 75.20314
> colSd(tmp5)
[1] 126.717991 9.691469 9.804910 6.626523 7.651135 11.289119
[7] 9.219024 8.534000 5.603421 10.700486 7.555532 5.911497
[13] 6.980471 8.760930 NA 6.077202 9.827034 9.018864
[19] 7.782202 8.671974
> colMax(tmp5)
[1] 469.29292 89.12400 87.19232 81.25145 78.29301 93.12952 89.96853
[8] 79.09390 81.00748 83.83299 81.39862 74.77780 81.17246 85.04668
[15] NA 79.19200 88.92496 86.29349 77.44704 81.82858
> colMin(tmp5)
[1] 53.13138 58.96732 57.60934 58.59399 56.38790 62.84909 61.07238 52.45497
[9] 64.82770 53.87534 60.34313 55.40136 61.05314 55.58273 NA 60.19625
[17] 59.05537 58.82742 53.59228 61.00961
>
> Max(tmp5,na.rm=TRUE)
[1] 469.2929
> Min(tmp5,na.rm=TRUE)
[1] 52.45497
> mean(tmp5,na.rm=TRUE)
[1] 73.10109
> Sum(tmp5,na.rm=TRUE)
[1] 14547.12
> Var(tmp5,na.rm=TRUE)
[1] 870.1122
>
> rowMeans(tmp5,na.rm=TRUE)
[1] 89.37630 67.90427 73.12221 69.48676 71.68502 71.37263 70.79806 72.86449
[9] 73.55346 70.66695
> rowSums(tmp5,na.rm=TRUE)
[1] 1787.526 1358.085 1462.444 1320.248 1433.700 1427.453 1415.961 1457.290
[9] 1471.069 1413.339
> rowVars(tmp5,na.rm=TRUE)
[1] 8035.62907 57.27718 80.79652 85.22327 69.11297 85.57967
[7] 74.96087 77.58471 71.49638 96.64399
> rowSd(tmp5,na.rm=TRUE)
[1] 89.641670 7.568169 8.988688 9.231645 8.313421 9.250928 8.657995
[8] 8.808218 8.455553 9.830767
> rowMax(tmp5,na.rm=TRUE)
[1] 469.29292 78.75934 84.68889 90.60395 86.29349 88.92496 85.21875
[8] 87.19232 89.74008 93.12952
> rowMin(tmp5,na.rm=TRUE)
[1] 52.45497 53.59228 55.28424 56.38790 55.58273 58.82742 53.87534 53.13138
[9] 58.63558 57.60934
>
> colMeans(tmp5,na.rm=TRUE)
[1] 110.01952 73.44414 73.33788 71.79760 68.73292 75.38037 74.62225
[8] 67.86172 70.92570 69.38137 71.47834 66.43136 71.37040 72.34080
[15] 71.00335 71.50762 71.78473 71.60851 66.51805 72.26530
> colSums(tmp5,na.rm=TRUE)
[1] 1100.1952 734.4414 733.3788 717.9760 687.3292 753.8037 746.2225
[8] 678.6172 709.2570 693.8137 714.7834 664.3136 713.7040 723.4080
[15] 639.0301 715.0762 717.8473 716.0851 665.1805 722.6530
> colVars(tmp5,na.rm=TRUE)
[1] 16057.44922 93.92457 96.13626 43.91081 58.53987 127.44421
[7] 84.99041 72.82916 31.39833 114.50040 57.08606 34.94579
[13] 48.72697 76.75390 89.07626 36.93238 96.57059 81.33990
[19] 60.56267 75.20314
> colSd(tmp5,na.rm=TRUE)
[1] 126.717991 9.691469 9.804910 6.626523 7.651135 11.289119
[7] 9.219024 8.534000 5.603421 10.700486 7.555532 5.911497
[13] 6.980471 8.760930 9.438022 6.077202 9.827034 9.018864
[19] 7.782202 8.671974
> colMax(tmp5,na.rm=TRUE)
[1] 469.29292 89.12400 87.19232 81.25145 78.29301 93.12952 89.96853
[8] 79.09390 81.00748 83.83299 81.39862 74.77780 81.17246 85.04668
[15] 83.72521 79.19200 88.92496 86.29349 77.44704 81.82858
> colMin(tmp5,na.rm=TRUE)
[1] 53.13138 58.96732 57.60934 58.59399 56.38790 62.84909 61.07238 52.45497
[9] 64.82770 53.87534 60.34313 55.40136 61.05314 55.58273 58.46130 60.19625
[17] 59.05537 58.82742 53.59228 61.00961
>
> # now set an entire row to NA
>
> tmp5[which.row,] <- NA
> rowMeans(tmp5,na.rm=TRUE)
[1] 89.37630 67.90427 73.12221 NaN 71.68502 71.37263 70.79806 72.86449
[9] 73.55346 70.66695
> rowSums(tmp5,na.rm=TRUE)
[1] 1787.526 1358.085 1462.444 0.000 1433.700 1427.453 1415.961 1457.290
[9] 1471.069 1413.339
> rowVars(tmp5,na.rm=TRUE)
[1] 8035.62907 57.27718 80.79652 NA 69.11297 85.57967
[7] 74.96087 77.58471 71.49638 96.64399
> rowSd(tmp5,na.rm=TRUE)
[1] 89.641670 7.568169 8.988688 NA 8.313421 9.250928 8.657995
[8] 8.808218 8.455553 9.830767
> rowMax(tmp5,na.rm=TRUE)
[1] 469.29292 78.75934 84.68889 NA 86.29349 88.92496 85.21875
[8] 87.19232 89.74008 93.12952
> rowMin(tmp5,na.rm=TRUE)
[1] 52.45497 53.59228 55.28424 NA 55.58273 58.82742 53.87534 53.13138
[9] 58.63558 57.60934
>
>
> # now set an entire col to NA
>
>
> tmp5[,which.col] <- NA
> colMeans(tmp5,na.rm=TRUE)
[1] 112.17681 75.05267 72.50756 73.26467 70.10459 75.77821 74.67563
[8] 67.20741 69.88235 69.78522 72.39680 66.49416 70.97887 73.08899
[15] NaN 72.13200 72.50855 72.93185 66.47954 71.20271
> colSums(tmp5,na.rm=TRUE)
[1] 1009.5913 675.4741 652.5681 659.3820 630.9413 682.0039 672.0806
[8] 604.8667 628.9411 628.0669 651.5712 598.4474 638.8098 657.8009
[15] 0.0000 649.1880 652.5770 656.3866 598.3159 640.8244
> colVars(tmp5,na.rm=TRUE)
[1] 18012.27421 76.55705 100.39732 25.18641 44.69073 141.59406
[7] 95.58216 77.11651 23.07665 126.97820 54.73168 39.26965
[13] 53.09326 80.05052 NA 37.16301 102.74782 71.80627
[19] 68.11632 71.90126
> colSd(tmp5,na.rm=TRUE)
[1] 134.209814 8.749689 10.019846 5.018606 6.685113 11.899330
[7] 9.776613 8.781601 4.803816 11.268460 7.398086 6.266550
[13] 7.286512 8.947095 NA 6.096147 10.136460 8.473858
[19] 8.253261 8.479461
> colMax(tmp5,na.rm=TRUE)
[1] 469.29292 89.12400 87.19232 81.25145 78.29301 93.12952 89.96853
[8] 79.09390 81.00748 83.83299 81.39862 74.77780 81.17246 85.04668
[15] -Inf 79.19200 88.92496 86.29349 77.44704 80.59855
> colMin(tmp5,na.rm=TRUE)
[1] 53.13138 64.99138 57.60934 67.11612 59.12891 62.84909 61.07238 52.45497
[9] 64.82770 53.87534 60.34313 55.40136 61.05314 55.58273 Inf 60.19625
[17] 59.05537 58.82742 53.59228 61.00961
>
>
>
>
> 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] 189.8172 235.8110 271.1827 209.6844 212.8067 196.7636 228.6284 163.9169
[9] 241.2413 197.0718
> apply(copymatrix,1,var,na.rm=TRUE)
[1] 189.8172 235.8110 271.1827 209.6844 212.8067 196.7636 228.6284 163.9169
[9] 241.2413 197.0718
>
>
>
> 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] -1.136868e-13 8.526513e-14 1.989520e-13 -5.684342e-14 -1.421085e-14
[6] 1.136868e-13 -8.526513e-14 -1.136868e-13 -5.684342e-14 -1.136868e-13
[11] 1.705303e-13 2.842171e-14 -5.684342e-14 1.421085e-13 0.000000e+00
[16] -1.705303e-13 1.136868e-13 2.842171e-14 0.000000e+00 0.000000e+00
>
>
>
>
>
>
>
>
>
>
> ## 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)
+ }
8 4
6 3
9 18
4 5
2 15
1 6
8 12
7 17
3 16
3 16
2 11
5 11
4 2
1 17
4 17
8 10
6 2
8 9
7 19
1 1
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] 2.00119
> Min(tmp)
[1] -1.956607
> mean(tmp)
[1] -0.1596849
> Sum(tmp)
[1] -15.96849
> Var(tmp)
[1] 1.038901
>
> rowMeans(tmp)
[1] -0.1596849
> rowSums(tmp)
[1] -15.96849
> rowVars(tmp)
[1] 1.038901
> rowSd(tmp)
[1] 1.019265
> rowMax(tmp)
[1] 2.00119
> rowMin(tmp)
[1] -1.956607
>
> colMeans(tmp)
[1] -0.02506444 0.94963434 -0.69718359 0.48158788 -1.50635732 -0.99458703
[7] -0.68470896 -0.26998774 1.47837870 0.18231673 0.58388747 0.34238546
[13] -0.33754507 -0.16202593 -0.80680220 -1.04628923 1.69967121 -1.74025251
[19] 0.22622234 0.44398976 0.47455980 -1.26003808 -0.87570944 -0.06111066
[25] -0.89031261 1.17875051 0.10297215 -0.53277115 0.23340794 1.51262894
[31] 2.00118951 -0.55046222 0.88176132 -0.72561496 -1.28710310 1.24901009
[37] -1.47314376 -1.33792301 -0.63577622 0.28930512 1.29401375 1.73589093
[43] 0.34267672 1.18987359 -1.39095316 -1.93492402 -0.86462818 -1.04629085
[49] -1.95306747 1.15212553 0.09857801 0.86274739 -0.58744754 0.62976108
[55] 1.19559429 0.18016967 -0.22897548 -0.04324310 -0.85831400 -0.96226332
[61] 1.75781503 -0.72694690 1.90989975 -0.24191512 0.12439316 0.26134512
[67] 0.39510898 0.87529576 -0.35572290 1.48321011 -1.44993928 -0.53856756
[73] -1.81655866 -1.23138997 0.22723513 0.24470385 -1.83922115 0.40748292
[79] -1.04648316 -1.52825122 -0.15037950 -1.10269577 -1.15883390 -0.67039537
[85] 0.25254746 -0.97927595 -1.32523358 0.25397417 -0.30041399 -1.95660680
[91] -0.96232557 0.19549957 0.89327839 0.60350218 -1.71949416 0.51110595
[97] -1.30838604 0.97854810 -1.13634909 0.97973704
> colSums(tmp)
[1] -0.02506444 0.94963434 -0.69718359 0.48158788 -1.50635732 -0.99458703
[7] -0.68470896 -0.26998774 1.47837870 0.18231673 0.58388747 0.34238546
[13] -0.33754507 -0.16202593 -0.80680220 -1.04628923 1.69967121 -1.74025251
[19] 0.22622234 0.44398976 0.47455980 -1.26003808 -0.87570944 -0.06111066
[25] -0.89031261 1.17875051 0.10297215 -0.53277115 0.23340794 1.51262894
[31] 2.00118951 -0.55046222 0.88176132 -0.72561496 -1.28710310 1.24901009
[37] -1.47314376 -1.33792301 -0.63577622 0.28930512 1.29401375 1.73589093
[43] 0.34267672 1.18987359 -1.39095316 -1.93492402 -0.86462818 -1.04629085
[49] -1.95306747 1.15212553 0.09857801 0.86274739 -0.58744754 0.62976108
[55] 1.19559429 0.18016967 -0.22897548 -0.04324310 -0.85831400 -0.96226332
[61] 1.75781503 -0.72694690 1.90989975 -0.24191512 0.12439316 0.26134512
[67] 0.39510898 0.87529576 -0.35572290 1.48321011 -1.44993928 -0.53856756
[73] -1.81655866 -1.23138997 0.22723513 0.24470385 -1.83922115 0.40748292
[79] -1.04648316 -1.52825122 -0.15037950 -1.10269577 -1.15883390 -0.67039537
[85] 0.25254746 -0.97927595 -1.32523358 0.25397417 -0.30041399 -1.95660680
[91] -0.96232557 0.19549957 0.89327839 0.60350218 -1.71949416 0.51110595
[97] -1.30838604 0.97854810 -1.13634909 0.97973704
> 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] -0.02506444 0.94963434 -0.69718359 0.48158788 -1.50635732 -0.99458703
[7] -0.68470896 -0.26998774 1.47837870 0.18231673 0.58388747 0.34238546
[13] -0.33754507 -0.16202593 -0.80680220 -1.04628923 1.69967121 -1.74025251
[19] 0.22622234 0.44398976 0.47455980 -1.26003808 -0.87570944 -0.06111066
[25] -0.89031261 1.17875051 0.10297215 -0.53277115 0.23340794 1.51262894
[31] 2.00118951 -0.55046222 0.88176132 -0.72561496 -1.28710310 1.24901009
[37] -1.47314376 -1.33792301 -0.63577622 0.28930512 1.29401375 1.73589093
[43] 0.34267672 1.18987359 -1.39095316 -1.93492402 -0.86462818 -1.04629085
[49] -1.95306747 1.15212553 0.09857801 0.86274739 -0.58744754 0.62976108
[55] 1.19559429 0.18016967 -0.22897548 -0.04324310 -0.85831400 -0.96226332
[61] 1.75781503 -0.72694690 1.90989975 -0.24191512 0.12439316 0.26134512
[67] 0.39510898 0.87529576 -0.35572290 1.48321011 -1.44993928 -0.53856756
[73] -1.81655866 -1.23138997 0.22723513 0.24470385 -1.83922115 0.40748292
[79] -1.04648316 -1.52825122 -0.15037950 -1.10269577 -1.15883390 -0.67039537
[85] 0.25254746 -0.97927595 -1.32523358 0.25397417 -0.30041399 -1.95660680
[91] -0.96232557 0.19549957 0.89327839 0.60350218 -1.71949416 0.51110595
[97] -1.30838604 0.97854810 -1.13634909 0.97973704
> colMin(tmp)
[1] -0.02506444 0.94963434 -0.69718359 0.48158788 -1.50635732 -0.99458703
[7] -0.68470896 -0.26998774 1.47837870 0.18231673 0.58388747 0.34238546
[13] -0.33754507 -0.16202593 -0.80680220 -1.04628923 1.69967121 -1.74025251
[19] 0.22622234 0.44398976 0.47455980 -1.26003808 -0.87570944 -0.06111066
[25] -0.89031261 1.17875051 0.10297215 -0.53277115 0.23340794 1.51262894
[31] 2.00118951 -0.55046222 0.88176132 -0.72561496 -1.28710310 1.24901009
[37] -1.47314376 -1.33792301 -0.63577622 0.28930512 1.29401375 1.73589093
[43] 0.34267672 1.18987359 -1.39095316 -1.93492402 -0.86462818 -1.04629085
[49] -1.95306747 1.15212553 0.09857801 0.86274739 -0.58744754 0.62976108
[55] 1.19559429 0.18016967 -0.22897548 -0.04324310 -0.85831400 -0.96226332
[61] 1.75781503 -0.72694690 1.90989975 -0.24191512 0.12439316 0.26134512
[67] 0.39510898 0.87529576 -0.35572290 1.48321011 -1.44993928 -0.53856756
[73] -1.81655866 -1.23138997 0.22723513 0.24470385 -1.83922115 0.40748292
[79] -1.04648316 -1.52825122 -0.15037950 -1.10269577 -1.15883390 -0.67039537
[85] 0.25254746 -0.97927595 -1.32523358 0.25397417 -0.30041399 -1.95660680
[91] -0.96232557 0.19549957 0.89327839 0.60350218 -1.71949416 0.51110595
[97] -1.30838604 0.97854810 -1.13634909 0.97973704
> colMedians(tmp)
[1] -0.02506444 0.94963434 -0.69718359 0.48158788 -1.50635732 -0.99458703
[7] -0.68470896 -0.26998774 1.47837870 0.18231673 0.58388747 0.34238546
[13] -0.33754507 -0.16202593 -0.80680220 -1.04628923 1.69967121 -1.74025251
[19] 0.22622234 0.44398976 0.47455980 -1.26003808 -0.87570944 -0.06111066
[25] -0.89031261 1.17875051 0.10297215 -0.53277115 0.23340794 1.51262894
[31] 2.00118951 -0.55046222 0.88176132 -0.72561496 -1.28710310 1.24901009
[37] -1.47314376 -1.33792301 -0.63577622 0.28930512 1.29401375 1.73589093
[43] 0.34267672 1.18987359 -1.39095316 -1.93492402 -0.86462818 -1.04629085
[49] -1.95306747 1.15212553 0.09857801 0.86274739 -0.58744754 0.62976108
[55] 1.19559429 0.18016967 -0.22897548 -0.04324310 -0.85831400 -0.96226332
[61] 1.75781503 -0.72694690 1.90989975 -0.24191512 0.12439316 0.26134512
[67] 0.39510898 0.87529576 -0.35572290 1.48321011 -1.44993928 -0.53856756
[73] -1.81655866 -1.23138997 0.22723513 0.24470385 -1.83922115 0.40748292
[79] -1.04648316 -1.52825122 -0.15037950 -1.10269577 -1.15883390 -0.67039537
[85] 0.25254746 -0.97927595 -1.32523358 0.25397417 -0.30041399 -1.95660680
[91] -0.96232557 0.19549957 0.89327839 0.60350218 -1.71949416 0.51110595
[97] -1.30838604 0.97854810 -1.13634909 0.97973704
> colRanges(tmp)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] -0.02506444 0.9496343 -0.6971836 0.4815879 -1.506357 -0.994587 -0.684709
[2,] -0.02506444 0.9496343 -0.6971836 0.4815879 -1.506357 -0.994587 -0.684709
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] -0.2699877 1.478379 0.1823167 0.5838875 0.3423855 -0.3375451 -0.1620259
[2,] -0.2699877 1.478379 0.1823167 0.5838875 0.3423855 -0.3375451 -0.1620259
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] -0.8068022 -1.046289 1.699671 -1.740253 0.2262223 0.4439898 0.4745598
[2,] -0.8068022 -1.046289 1.699671 -1.740253 0.2262223 0.4439898 0.4745598
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
[1,] -1.260038 -0.8757094 -0.06111066 -0.8903126 1.178751 0.1029721 -0.5327712
[2,] -1.260038 -0.8757094 -0.06111066 -0.8903126 1.178751 0.1029721 -0.5327712
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
[1,] 0.2334079 1.512629 2.00119 -0.5504622 0.8817613 -0.725615 -1.287103
[2,] 0.2334079 1.512629 2.00119 -0.5504622 0.8817613 -0.725615 -1.287103
[,36] [,37] [,38] [,39] [,40] [,41] [,42]
[1,] 1.24901 -1.473144 -1.337923 -0.6357762 0.2893051 1.294014 1.735891
[2,] 1.24901 -1.473144 -1.337923 -0.6357762 0.2893051 1.294014 1.735891
[,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,] 0.3426767 1.189874 -1.390953 -1.934924 -0.8646282 -1.046291 -1.953067
[2,] 0.3426767 1.189874 -1.390953 -1.934924 -0.8646282 -1.046291 -1.953067
[,50] [,51] [,52] [,53] [,54] [,55] [,56]
[1,] 1.152126 0.09857801 0.8627474 -0.5874475 0.6297611 1.195594 0.1801697
[2,] 1.152126 0.09857801 0.8627474 -0.5874475 0.6297611 1.195594 0.1801697
[,57] [,58] [,59] [,60] [,61] [,62] [,63]
[1,] -0.2289755 -0.0432431 -0.858314 -0.9622633 1.757815 -0.7269469 1.9099
[2,] -0.2289755 -0.0432431 -0.858314 -0.9622633 1.757815 -0.7269469 1.9099
[,64] [,65] [,66] [,67] [,68] [,69] [,70]
[1,] -0.2419151 0.1243932 0.2613451 0.395109 0.8752958 -0.3557229 1.48321
[2,] -0.2419151 0.1243932 0.2613451 0.395109 0.8752958 -0.3557229 1.48321
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
[1,] -1.449939 -0.5385676 -1.816559 -1.23139 0.2272351 0.2447038 -1.839221
[2,] -1.449939 -0.5385676 -1.816559 -1.23139 0.2272351 0.2447038 -1.839221
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
[1,] 0.4074829 -1.046483 -1.528251 -0.1503795 -1.102696 -1.158834 -0.6703954
[2,] 0.4074829 -1.046483 -1.528251 -0.1503795 -1.102696 -1.158834 -0.6703954
[,85] [,86] [,87] [,88] [,89] [,90] [,91]
[1,] 0.2525475 -0.9792759 -1.325234 0.2539742 -0.300414 -1.956607 -0.9623256
[2,] 0.2525475 -0.9792759 -1.325234 0.2539742 -0.300414 -1.956607 -0.9623256
[,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] 0.1954996 0.8932784 0.6035022 -1.719494 0.511106 -1.308386 0.9785481
[2,] 0.1954996 0.8932784 0.6035022 -1.719494 0.511106 -1.308386 0.9785481
[,99] [,100]
[1,] -1.136349 0.979737
[2,] -1.136349 0.979737
>
>
> Max(tmp2)
[1] 2.331976
> Min(tmp2)
[1] -2.799075
> mean(tmp2)
[1] 0.1141884
> Sum(tmp2)
[1] 11.41884
> Var(tmp2)
[1] 1.19194
>
> rowMeans(tmp2)
[1] -1.47248231 0.54374381 0.07746158 1.08921758 0.47361569 0.09355249
[7] 0.24040506 -0.42100621 -0.20259890 0.69758163 -0.08572063 2.09319258
[13] 0.63395435 -0.14050198 -2.07321258 -0.11349227 -1.27802613 0.41625121
[19] 0.30600615 -0.34258221 0.82593522 0.66723365 2.21908988 1.26036491
[25] 1.44660665 0.04767367 -0.56395822 1.32961123 0.85192692 0.76052839
[31] 0.29493392 -2.20214153 1.09915029 0.88335914 -0.80348747 0.54016231
[37] -1.42717806 -0.03504178 0.21590280 -0.35309490 2.30443923 -0.34775130
[43] 0.83299178 1.23874548 0.81735615 1.64531412 1.41101298 0.09286307
[49] -0.50426755 0.20728422 2.00949076 -0.18871982 0.21412225 -0.39542918
[55] 1.07359856 1.28932983 2.04728897 -0.21274395 0.25452399 0.50528961
[61] -0.73361359 1.39011250 1.20866928 2.33197582 -0.38043482 -0.81855609
[67] 1.32861554 1.55917211 1.00967064 -0.71393931 -0.11298474 -1.46365439
[73] 0.59535896 -0.64780460 -0.86768488 -0.88541571 -0.48484733 0.57111390
[79] -1.86033549 0.44174319 0.62064292 0.14524832 0.39812147 0.43761605
[85] 0.13776191 -0.57901215 -0.18401722 -2.58374449 -2.79907532 -0.89797911
[91] -1.01597131 0.38450464 0.89560076 -2.47992448 0.21071539 -0.88726509
[97] -0.27022171 -0.25624432 -1.70853004 -1.50422060
> rowSums(tmp2)
[1] -1.47248231 0.54374381 0.07746158 1.08921758 0.47361569 0.09355249
[7] 0.24040506 -0.42100621 -0.20259890 0.69758163 -0.08572063 2.09319258
[13] 0.63395435 -0.14050198 -2.07321258 -0.11349227 -1.27802613 0.41625121
[19] 0.30600615 -0.34258221 0.82593522 0.66723365 2.21908988 1.26036491
[25] 1.44660665 0.04767367 -0.56395822 1.32961123 0.85192692 0.76052839
[31] 0.29493392 -2.20214153 1.09915029 0.88335914 -0.80348747 0.54016231
[37] -1.42717806 -0.03504178 0.21590280 -0.35309490 2.30443923 -0.34775130
[43] 0.83299178 1.23874548 0.81735615 1.64531412 1.41101298 0.09286307
[49] -0.50426755 0.20728422 2.00949076 -0.18871982 0.21412225 -0.39542918
[55] 1.07359856 1.28932983 2.04728897 -0.21274395 0.25452399 0.50528961
[61] -0.73361359 1.39011250 1.20866928 2.33197582 -0.38043482 -0.81855609
[67] 1.32861554 1.55917211 1.00967064 -0.71393931 -0.11298474 -1.46365439
[73] 0.59535896 -0.64780460 -0.86768488 -0.88541571 -0.48484733 0.57111390
[79] -1.86033549 0.44174319 0.62064292 0.14524832 0.39812147 0.43761605
[85] 0.13776191 -0.57901215 -0.18401722 -2.58374449 -2.79907532 -0.89797911
[91] -1.01597131 0.38450464 0.89560076 -2.47992448 0.21071539 -0.88726509
[97] -0.27022171 -0.25624432 -1.70853004 -1.50422060
> 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] -1.47248231 0.54374381 0.07746158 1.08921758 0.47361569 0.09355249
[7] 0.24040506 -0.42100621 -0.20259890 0.69758163 -0.08572063 2.09319258
[13] 0.63395435 -0.14050198 -2.07321258 -0.11349227 -1.27802613 0.41625121
[19] 0.30600615 -0.34258221 0.82593522 0.66723365 2.21908988 1.26036491
[25] 1.44660665 0.04767367 -0.56395822 1.32961123 0.85192692 0.76052839
[31] 0.29493392 -2.20214153 1.09915029 0.88335914 -0.80348747 0.54016231
[37] -1.42717806 -0.03504178 0.21590280 -0.35309490 2.30443923 -0.34775130
[43] 0.83299178 1.23874548 0.81735615 1.64531412 1.41101298 0.09286307
[49] -0.50426755 0.20728422 2.00949076 -0.18871982 0.21412225 -0.39542918
[55] 1.07359856 1.28932983 2.04728897 -0.21274395 0.25452399 0.50528961
[61] -0.73361359 1.39011250 1.20866928 2.33197582 -0.38043482 -0.81855609
[67] 1.32861554 1.55917211 1.00967064 -0.71393931 -0.11298474 -1.46365439
[73] 0.59535896 -0.64780460 -0.86768488 -0.88541571 -0.48484733 0.57111390
[79] -1.86033549 0.44174319 0.62064292 0.14524832 0.39812147 0.43761605
[85] 0.13776191 -0.57901215 -0.18401722 -2.58374449 -2.79907532 -0.89797911
[91] -1.01597131 0.38450464 0.89560076 -2.47992448 0.21071539 -0.88726509
[97] -0.27022171 -0.25624432 -1.70853004 -1.50422060
> rowMin(tmp2)
[1] -1.47248231 0.54374381 0.07746158 1.08921758 0.47361569 0.09355249
[7] 0.24040506 -0.42100621 -0.20259890 0.69758163 -0.08572063 2.09319258
[13] 0.63395435 -0.14050198 -2.07321258 -0.11349227 -1.27802613 0.41625121
[19] 0.30600615 -0.34258221 0.82593522 0.66723365 2.21908988 1.26036491
[25] 1.44660665 0.04767367 -0.56395822 1.32961123 0.85192692 0.76052839
[31] 0.29493392 -2.20214153 1.09915029 0.88335914 -0.80348747 0.54016231
[37] -1.42717806 -0.03504178 0.21590280 -0.35309490 2.30443923 -0.34775130
[43] 0.83299178 1.23874548 0.81735615 1.64531412 1.41101298 0.09286307
[49] -0.50426755 0.20728422 2.00949076 -0.18871982 0.21412225 -0.39542918
[55] 1.07359856 1.28932983 2.04728897 -0.21274395 0.25452399 0.50528961
[61] -0.73361359 1.39011250 1.20866928 2.33197582 -0.38043482 -0.81855609
[67] 1.32861554 1.55917211 1.00967064 -0.71393931 -0.11298474 -1.46365439
[73] 0.59535896 -0.64780460 -0.86768488 -0.88541571 -0.48484733 0.57111390
[79] -1.86033549 0.44174319 0.62064292 0.14524832 0.39812147 0.43761605
[85] 0.13776191 -0.57901215 -0.18401722 -2.58374449 -2.79907532 -0.89797911
[91] -1.01597131 0.38450464 0.89560076 -2.47992448 0.21071539 -0.88726509
[97] -0.27022171 -0.25624432 -1.70853004 -1.50422060
>
> colMeans(tmp2)
[1] 0.1141884
> colSums(tmp2)
[1] 11.41884
> colVars(tmp2)
[1] 1.19194
> colSd(tmp2)
[1] 1.09176
> colMax(tmp2)
[1] 2.331976
> colMin(tmp2)
[1] -2.799075
> colMedians(tmp2)
[1] 0.2089998
> colRanges(tmp2)
[,1]
[1,] -2.799075
[2,] 2.331976
>
> 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] -1.2000574 1.3846308 0.8439013 -3.8415904 -0.2686073 -0.8004846
[7] -2.3071323 2.2315443 -0.9749857 -2.1012508
> colApply(tmp,quantile)[,1]
[,1]
[1,] -1.57623445
[2,] -0.59310554
[3,] 0.08462555
[4,] 0.27557636
[5,] 1.02038727
>
> rowApply(tmp,sum)
[1] -0.7697665 1.7780731 1.8367404 5.3082415 -8.3878834 -1.1121264
[7] 0.2586634 -1.5785020 -2.7917926 -1.5756799
> rowApply(tmp,rank)[1:10,]
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 7 2 6 1 10 5 6 5 7 1
[2,] 10 9 2 5 9 3 4 6 1 8
[3,] 4 8 1 7 2 7 10 9 6 7
[4,] 2 6 4 2 3 6 8 2 3 10
[5,] 8 5 5 10 5 10 5 4 5 2
[6,] 6 10 7 4 1 9 3 3 2 9
[7,] 5 1 10 3 8 2 9 1 4 4
[8,] 9 7 3 8 6 4 7 7 8 6
[9,] 3 4 9 6 4 8 1 10 10 5
[10,] 1 3 8 9 7 1 2 8 9 3
>
> tmp <- createBufferedMatrix(5,20)
>
> tmp[1:5,1:20] <- rnorm(100)
> colApply(tmp,sum)
[1] 2.2741597 1.0699503 0.1768138 -2.4093714 -1.0348339 0.1856544
[7] 0.6234098 0.1505602 0.1380352 3.2225149 -0.7293216 -1.6469323
[13] -2.8297049 -0.8646869 2.8393095 3.4016511 2.1964850 0.7338969
[19] -0.9711976 -1.6901012
> colApply(tmp,quantile)[,1]
[,1]
[1,] -0.5540421
[2,] -0.3462768
[3,] 0.4909631
[4,] 1.1957047
[5,] 1.4878107
>
> rowApply(tmp,sum)
[1] 2.136146 -3.085595 1.307263 2.506176 1.972300
> rowApply(tmp,rank)[1:5,]
[,1] [,2] [,3] [,4] [,5]
[1,] 16 20 9 19 5
[2,] 13 8 15 16 6
[3,] 6 9 14 17 4
[4,] 17 6 6 14 1
[5,] 14 18 10 1 7
>
>
> as.matrix(tmp)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.4909631 0.2870793 -0.2377977 0.6995438 0.4139828 0.1983508
[2,] 1.4878107 -0.5915567 -0.4496500 -0.7163189 1.4381217 -1.0783466
[3,] -0.3462768 1.1780271 0.5802439 -0.7944047 -0.3216031 0.5107870
[4,] 1.1957047 0.6735329 0.9547823 0.5419945 -2.2508156 0.4161958
[5,] -0.5540421 -0.4771323 -0.6707648 -2.1401861 -0.3145198 0.1386674
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] -0.08994561 1.2004161 0.2447128 1.3176385 -0.32807059 -0.02024724
[2,] -1.55565399 -1.2845684 -0.3874157 1.4675056 -0.27525768 -0.68736960
[3,] 0.44047613 -1.1139659 -1.1947190 0.2056496 -0.72143285 -2.03734235
[4,] 0.32637343 1.1403897 -0.6853740 0.4340877 0.08708229 0.23802456
[5,] 1.50215985 0.2082887 2.1608312 -0.2023665 0.50835723 0.86000230
[,13] [,14] [,15] [,16] [,17] [,18]
[1,] -1.1384860 -1.4665508 0.4442300 -0.5775248 1.1469694 -0.8168069
[2,] -0.9667622 -0.1685287 0.9264869 -0.1130003 1.0636783 -1.7015388
[3,] -0.5378557 1.6334946 1.4332227 1.5150543 1.3351650 1.4784796
[4,] -0.4513799 -0.7365913 -1.5296752 2.0349760 0.2801211 0.2928992
[5,] 0.2647789 -0.1265106 1.5650452 0.5421459 -1.6294489 1.4808638
[,19] [,20]
[1,] 0.27678372 0.09090548
[2,] 0.05534126 0.45142847
[3,] -1.02544777 -0.91028831
[4,] 0.59356486 -1.04971702
[5,] -0.87143966 -0.27242985
>
>
> is.BufferedMatrix(tmp)
[1] TRUE
>
> as.BufferedMatrix(as.matrix(tmp))
BufferedMatrix object
Matrix size: 5 20
Buffer size: 1 1
Directory: /home/biocbuild/bbs-3.18-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: /home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 652 bytes.
Disk usage : 200 bytes.
> subBufferedMatrix(tmp,,5:8)
BufferedMatrix object
Matrix size: 5 4
Buffer size: 1 1
Directory: /home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests
Prefix: BM
Mode: Col mode
Read Only: FALSE
Memory usage : 566 bytes.
Disk usage : 160 bytes.
> subBufferedMatrix(tmp,1:3,)
BufferedMatrix object
Matrix size: 3 20
Buffer size: 1 1
Directory: /home/biocbuild/bbs-3.18-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 -1.070549 -0.001744516 -1.011965 -1.00261 -0.07139745 -0.3190884 -1.742216
col8 col9 col10 col11 col12 col13 col14
row1 -0.3879577 1.289279 1.133516 1.741252 -0.6060227 1.04375 0.4824533
col15 col16 col17 col18 col19 col20
row1 -0.3813843 -0.1475919 -1.073422 1.009792 0.1310311 0.6173223
> tmp[,"col10"]
col10
row1 1.1335159
row2 -1.5034486
row3 2.0421357
row4 0.9060089
row5 -0.5358914
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6
row1 -1.0705486 -0.001744516 -1.0119645 -1.0026099 -0.07139745 -0.3190884
row5 -0.4199158 0.202109747 -0.4068481 -0.6889913 -0.55059913 -0.1377305
col7 col8 col9 col10 col11 col12 col13
row1 -1.7422160 -0.3879577 1.2892791 1.1335159 1.741252 -0.6060227 1.04374992
row5 -0.3353284 1.1503948 -0.8459278 -0.5358914 1.744873 -0.8291506 0.08806823
col14 col15 col16 col17 col18 col19 col20
row1 0.4824533 -0.3813843 -0.1475919 -1.0734225 1.0097922 0.1310311 0.6173223
row5 0.4877779 -1.0988513 0.8395384 -0.5077372 -0.5789798 0.3655817 -1.0879928
> tmp[,c("col6","col20")]
col6 col20
row1 -0.3190884 0.6173223
row2 0.3151334 0.1569474
row3 -0.3767463 0.9492248
row4 -0.4801691 0.9961869
row5 -0.1377305 -1.0879928
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 -0.3190884 0.6173223
row5 -0.1377305 -1.0879928
>
>
>
>
> 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 49.19667 50.68284 49.72185 51.82048 50.59795 106.7775 50.90081 50.79292
col9 col10 col11 col12 col13 col14 col15 col16
row1 50.96746 49.66875 50.36557 51.06793 49.74445 48.18455 50.73294 48.04533
col17 col18 col19 col20
row1 48.56388 49.95836 49.9724 104.2096
> tmp[,"col10"]
col10
row1 49.66875
row2 28.94884
row3 29.38763
row4 30.59991
row5 49.57687
> tmp[c("row1","row5"),]
col1 col2 col3 col4 col5 col6 col7 col8
row1 49.19667 50.68284 49.72185 51.82048 50.59795 106.7775 50.90081 50.79292
row5 50.17273 49.80943 50.82282 50.19560 49.17239 105.7015 50.30561 49.21441
col9 col10 col11 col12 col13 col14 col15 col16
row1 50.96746 49.66875 50.36557 51.06793 49.74445 48.18455 50.73294 48.04533
row5 48.96231 49.57687 49.78355 50.33986 50.35678 50.22023 51.04170 48.82017
col17 col18 col19 col20
row1 48.56388 49.95836 49.97240 104.2096
row5 48.39936 49.95133 50.03991 105.6890
> tmp[,c("col6","col20")]
col6 col20
row1 106.77754 104.20956
row2 75.04806 76.73129
row3 75.53551 72.71493
row4 75.81856 75.46814
row5 105.70152 105.68903
> tmp[c("row1","row5"),c("col6","col20")]
col6 col20
row1 106.7775 104.2096
row5 105.7015 105.6890
>
>
> subBufferedMatrix(tmp,c("row1","row5"),c("col6","col20"))[1:2,1:2]
col6 col20
row1 106.7775 104.2096
row5 105.7015 105.6890
>
>
>
>
>
> tmp <- createBufferedMatrix(5,20)
> tmp[1:5,1:20] <- rnorm(100)
> colnames(tmp) <- colnames(tmp,do.NULL=FALSE)
>
> tmp[,"col13"]
col13
[1,] 0.5662898
[2,] 1.8211820
[3,] -1.1181934
[4,] 1.4385103
[5,] 1.7414173
> tmp[,c("col17","col7")]
col17 col7
[1,] -1.3014233 -0.8226466
[2,] 0.4698864 -0.3524602
[3,] -0.6592351 0.9564435
[4,] 1.2410178 1.0686122
[5,] -0.3862738 1.2353092
>
> subBufferedMatrix(tmp,,c("col6","col20"))[,1:2]
col6 col20
[1,] 1.04482815 0.1985259
[2,] -0.08026407 1.1220228
[3,] 0.11862161 -1.1824077
[4,] 0.29935237 0.3480941
[5,] -0.10239998 1.5258765
> subBufferedMatrix(tmp,1,c("col6"))[,1]
col1
[1,] 1.044828
> subBufferedMatrix(tmp,1:2,c("col6"))[,1]
col6
[1,] 1.04482815
[2,] -0.08026407
>
>
>
> 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 1.1811200 0.1051599 -1.09438156 -0.3349115 0.7413358 -1.06521589 0.4231199
row1 0.2725814 0.3438161 0.07503619 0.2855907 0.3076991 -0.03312608 1.1233402
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
row3 -0.4675202 1.9709377 1.5243858 -0.5406043 -0.1088275 0.6297093 1.1615253
row1 1.6979334 0.3809737 -0.1646039 -0.4872537 -0.7286342 -0.9168134 0.2417747
[,15] [,16] [,17] [,18] [,19] [,20]
row3 -0.4844481 -0.31366214 0.5329366 -1.7660728 1.657984 -2.250039
row1 -0.5678997 -0.05659588 1.3798806 0.1983542 1.767531 -1.826391
> subBufferedMatrix(tmp,c("row2"),1:10)[,1:10]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row2 0.752705 0.8728373 0.9485935 -1.157289 0.6282926 -1.713612 0.6626698
[,8] [,9] [,10]
row2 0.3755696 0.07130981 -0.7502975
> subBufferedMatrix(tmp,c("row5"),1:20)[,1:20]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
row5 0.2584419 -0.3144131 -1.690402 0.2070914 0.003161208 -1.712351 -2.110197
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
row5 0.009017564 0.6965106 -0.04864585 1.693519 1.237253 -1.640129 -1.107968
[,15] [,16] [,17] [,18] [,19] [,20]
row5 1.845448 -0.4165342 1.705961 -1.137981 1.439588 -0.4501109
>
>
> 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: 0x169b0860>
> is.ReadOnlyMode(tmp)
[1] TRUE
>
> filenames(tmp)
[1] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM17514678fecc5b"
[2] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM17514642b645bf"
[3] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM17514639cb336e"
[4] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM17514626fa5663"
[5] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751467468d36f"
[6] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751465f27952f"
[7] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751467ed77489"
[8] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751464d92d408"
[9] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM175146743c8324"
[10] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751461c7751c1"
[11] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM175146542eaf31"
[12] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM17514676ebd950"
[13] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751466596ded6"
[14] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM17514633a8e90a"
[15] "/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests/BM1751464a228e71"
>
>
> ### 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: 0x153a4210>
> MoveStorageDirectory(tmp,getwd(),full.path=TRUE)
<pointer: 0x153a4210>
Warning message:
In dir.create(new.directory) :
'/home/biocbuild/bbs-3.18-bioc/meat/BufferedMatrix.Rcheck/tests' already exists
>
>
> RowMode(tmp)
<pointer: 0x153a4210>
> rowMedians(tmp)
[1] 0.1323616543 0.2452645886 0.0527850109 0.0880353383 0.2880357903
[6] 0.0477623885 0.4357357387 -0.2340440766 0.4224929199 0.2882652325
[11] -0.2718753003 -0.6049704510 0.1529234875 0.0910349463 0.3289634133
[16] -0.0473733780 -0.0675411604 -0.0928328395 0.0099216919 0.4745862025
[21] 0.2511753602 0.3288543768 -0.3701419134 -0.0679311586 0.0883426352
[26] 0.0071509469 0.2839823879 0.4854816616 0.3657295983 0.1277256515
[31] -0.4625217128 -0.5402301887 0.0492378059 -0.0458381952 -0.6710045114
[36] -0.0087936847 -0.0734047030 0.0388184008 -0.1764559528 0.0148413974
[41] -0.6941622119 0.3925108095 -0.1550223116 0.0562228309 0.0921820136
[46] -0.1090314004 0.0861758858 -0.2448705736 0.0952058025 -0.2056458502
[51] 0.4261389667 -0.0213650387 -0.0523550262 0.1882243360 0.0982179480
[56] -0.3815444549 0.3686798407 0.3974618435 0.7917531034 0.2158222473
[61] 0.5336282092 0.1488104936 -0.1027987350 -0.0604648352 -0.2894289997
[66] -0.2068091073 -0.3321812432 0.0164645942 -0.4322677642 -0.0596013245
[71] -0.1812519066 -0.2768210068 0.2865689320 0.3768943657 0.3672076771
[76] 0.1902151809 0.2344809166 0.0478313311 -0.1602223815 -0.3335073608
[81] -0.3716135123 -0.5001536677 -0.1477811472 0.0376238383 0.1448410490
[86] 0.4253599336 -0.3805045481 0.0198610835 -0.1299151813 -0.2006124292
[91] 0.1238332029 0.0703938460 0.3083799426 0.7460590152 0.2529575142
[96] 0.2118769882 -0.0540828651 0.2069949485 0.3612024613 0.3553247234
[101] 0.1506089534 -0.4120253046 0.2891278377 -0.1627734361 0.0484377112
[106] -0.1534814917 0.1214516013 0.1205428957 -0.9684862377 0.3192717535
[111] 0.1994629002 -0.2972137052 0.5206928672 0.0347197297 0.1121278440
[116] -0.0876871282 0.1486196076 0.0176013211 0.7953461279 -0.3331828762
[121] 0.2104335190 -0.3024712416 -0.1737204035 0.4322912320 0.0609909550
[126] -0.2047605288 0.0769345333 -0.1790206080 0.1366103126 -0.5314659693
[131] 0.2589495859 -0.0816179721 -0.0352096681 -0.1124401683 -0.1421147970
[136] 0.6699986177 0.1776833024 0.5228393419 0.0153812310 0.0015797219
[141] 0.0658460544 0.4301902859 0.0636379258 0.1483368231 0.0654009762
[146] -0.0675403975 -0.4525049378 0.1226214094 -0.8495738807 -0.4094300858
[151] 0.1684975961 -0.1901781616 -0.1266533701 -0.3539733223 0.2432575315
[156] 0.1755934500 0.3583125866 0.0386436283 0.0604940039 0.2982916832
[161] 0.0781488220 0.0467486989 -0.0799950698 -0.3795048122 -0.0008060193
[166] -0.2176216275 0.0535991331 0.5515713169 -0.0231520497 -0.0876457522
[171] 0.5922930127 -0.0729254231 0.5547713366 0.2499942765 0.3205023035
[176] -0.4601031628 0.2004822239 -0.3801720396 -0.3240595058 0.1857683667
[181] 0.2080285714 0.8123639594 -0.5965823798 -0.0088978997 -0.2508645985
[186] 0.0167357010 0.1298095211 0.0663901169 0.1420284817 0.0714944988
[191] -0.1239254956 0.3375648011 0.1289960808 -0.2577263434 -0.3791260930
[196] 0.4820362787 -0.2396171851 0.2823803913 -0.2097893887 0.1079919508
[201] 0.0288970953 0.2310030971 0.0383896200 -0.1367456358 -0.2331246433
[206] 0.3432633794 -0.3226944122 -0.2413634987 0.1205112643 -0.4100791488
[211] -0.3640174383 -0.2307772610 0.2758234419 -0.3548411851 0.0750006675
[216] 0.1283237963 -0.1241778014 -0.0308003216 0.2569143199 0.4687364947
[221] -0.0751502192 0.3377410241 0.5204062009 -0.4498541128 0.5792041826
[226] -0.0887573018 -0.0758692534 0.5125311185 -0.3030475645 -0.4026215783
>
> proc.time()
user system elapsed
2.058 0.902 2.977
BufferedMatrix.Rcheck/tests/rawCalltesting.Rout
R version 4.3.0 (2023-04-21) -- "Already Tomorrow"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu (64-bit)
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: 0x36463770>
> .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: 0x36463770>
> .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: 0x36463770>
> .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: 0x36463770>
> 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: 0x35b65900>
> .Call("R_bm_AddColumn",P)
<pointer: 0x35b65900>
> .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: 0x35b65900>
> .Call("R_bm_AddColumn",P)
<pointer: 0x35b65900>
> .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: 0x35b65900>
> 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: 0x35c90630>
> .Call("R_bm_AddColumn",P)
<pointer: 0x35c90630>
> .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: 0x35c90630>
>
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x35c90630>
> .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: 0x35c90630>
>
> .Call("R_bm_RowMode",P)
<pointer: 0x35c90630>
> .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: 0x35c90630>
>
> .Call("R_bm_ColMode",P)
<pointer: 0x35c90630>
> .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: 0x35c90630>
> 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: 0x35938360>
> .Call("R_bm_SetPrefix",P,"BufferedMatrixFile")
<pointer: 0x35938360>
> .Call("R_bm_AddColumn",P)
<pointer: 0x35938360>
> .Call("R_bm_AddColumn",P)
<pointer: 0x35938360>
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFile17516611e8b69b" "BufferedMatrixFile17516644f4985d"
> rm(P)
> dir(pattern="BufferedMatrixFile")
[1] "BufferedMatrixFile17516611e8b69b" "BufferedMatrixFile17516644f4985d"
>
>
> P <- .Call("R_bm_Create",prefix,directory,1,1)
> .Call("R_bm_setRows",P,10)
[1] TRUE
> .Call("R_bm_AddColumn",P)
<pointer: 0x34780e90>
> .Call("R_bm_AddColumn",P)
<pointer: 0x34780e90>
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x34780e90>
> .Call("R_bm_isReadOnlyMode",P)
[1] TRUE
> .Call("R_bm_ReadOnlyModeToggle",P)
<pointer: 0x34780e90>
> .Call("R_bm_isReadOnlyMode",P)
[1] FALSE
> .Call("R_bm_isRowMode",P)
[1] FALSE
> .Call("R_bm_RowMode",P)
<pointer: 0x34780e90>
> .Call("R_bm_isRowMode",P)
[1] TRUE
> .Call("R_bm_ColMode",P)
<pointer: 0x34780e90>
> .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: 0x358d8d80>
> .Call("R_bm_AddColumn",P)
<pointer: 0x358d8d80>
>
> .Call("R_bm_getSize",P)
[1] 10 2
> .Call("R_bm_getBufferSize",P)
[1] 1 1
> .Call("R_bm_ResizeBuffer",P,5,5)
<pointer: 0x358d8d80>
>
> .Call("R_bm_getBufferSize",P)
[1] 5 5
> .Call("R_bm_ResizeBuffer",P,-1,5)
<pointer: 0x358d8d80>
> 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: 0x34a799a0>
> .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: 0x34a799a0>
> rm(P)
>
> proc.time()
user system elapsed
0.318 0.046 0.351
BufferedMatrix.Rcheck/tests/Rcodetesting.Rout
R version 4.3.0 (2023-04-21) -- "Already Tomorrow"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu (64-bit)
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
>
> Temp <- createBufferedMatrix(100)
> dim(Temp)
[1] 100 0
> buffer.dim(Temp)
[1] 1 1
>
>
> proc.time()
user system elapsed
0.335 0.034 0.357