Last updated on 2025-06-13 04:49:33 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-gcc | 2.0 | 6.12 | 41.76 | 47.88 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2.0 | 97.04 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0 | 1855.18 | ERROR | |||
| r-devel-windows-x86_64 | 2.0 | 15.00 | 101.00 | 116.00 | OK | |
| r-release-macos-arm64 | 2.0 | 79.00 | OK | |||
| r-release-macos-x86_64 | 1.5.7.2 | 128.00 | OK | |||
| r-oldrel-macos-arm64 | 2.0 | 71.00 | OK | |||
| r-oldrel-macos-x86_64 | 1.5.7.2 | 122.00 | OK | |||
| r-oldrel-windows-x86_64 | 2.0 | 19.00 | 104.00 | 123.00 | OK |
Version: 2.0
Check: examples
Result: ERROR
Running examples in ‘limSolve-Ex.R’ failed
The error most likely occurred in:
> ### Name: E_coli
> ### Title: An underdetermined linear inverse problem: the Escherichia Coli
> ### Core Metabolism Model.
> ### Aliases: E_coli
> ### Keywords: datasets
>
> ### ** Examples
>
> # 1. parsimonious (simplest) solution
> pars <- lsei(E = E_coli$A, F = E_coli$B,
+ G = E_coli$G, H = E_coli$H)$X
Warning in lsei(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H) :
LSEI error: inequalities contradictory
>
> # 2. the optimal solution - solved with linear programming
> # some unknowns can be negative
>
> LP <- linp(E = E_coli$A, F = E_coli$B,G = E_coli$G, H = E_coli$H,
+ Cost = -E_coli$Maximise, ispos = FALSE)
> (Optimal <- LP$X)
GLK1 PGI1 PFKA FBP FBA TPIA
0.000000 807.532745 781.590686 0.000000 781.590686 781.590686
GAPA PGK GPMA ENO PPSA PYKA
1541.434199 1541.434199 1492.089090 1492.089090 0.000000 466.657964
ACEE ZWF PGL GND RPIA RPE
1149.295284 0.000000 0.000000 0.000000 23.623833 -23.623833
TKTA1 TKTA2 TALA GLTA ACNA ICDA
-5.850762 -17.773070 -5.850762 35.435749 35.435749 35.435749
SUCA SUCC1 SDHA1 FRDA FUMA MDH
0.000000 0.000000 0.000000 100.000000 -100.000000 -100.000000
DLD1 ADHE2 PFLA PTA ACKA ACS
0.000000 1000.000000 10.000000 0.000000 0.000000 0.000000
PCKA PPC MAEB SFCA ACEA ACEB
0.000000 194.384939 0.000000 0.000000 0.000000 0.000000
PPA GLPK GPSA1 RBSK NUOA FDOH
0.000000 0.000000 0.000000 0.000000 140.000000 0.000000
GLPD CYOA SDHA2 PNT1A PNT2A ATPA
0.000000 40.000000 -100.000000 0.000000 567.965512 -145.466329
GLCUP GLCPTS GLUP RIBUP ACUP LACUP
0.000000 814.156250 0.000000 0.000000 0.000000 0.000000
FORUP ETHUP SUCCUP PYRUP PIUP O2TX
-10.000000 -1000.000000 -100.000000 -27.796342 120.547782 20.000000
CO2TX ATPM ADK Growth
-990.346093 5.870000 0.000000 33.117523
>
> # 3.ranges of all unknowns, including the central value and all solutions
> xr <- xranges(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H,
+ central = TRUE, full = TRUE)
Warning in lsei(E = E, F = F, G = G, H = H) :
LSEI error: inequalities contradictory
>
> # the central point is a valid solution:
> X <- xr[ ,"central"]
>
> max(abs(E_coli$A%*%X - E_coli$B))
[1] 3.979039e-13
> min(E_coli$G%*%X - E_coli$H)
[1] 0
>
> # 4. Sample solution space; the central value is a good starting point
> # for algorithms cda and rda - but these need many iterations
> ## Not run:
> ##D xs <- xsample(E = E_coli$A, F = E_coli$B, G = E_coli$G,H = E_coli$H,
> ##D iter = 50000, out = 5000, type = "rda", x0 = X)$X
> ##D pairs(xs[ ,10:20], pch = ".", cex = 2, main = "sampling, using rda")
> ## End(Not run)
>
> # using mirror algorithm takes less iterations,
> # but an iteration takes more time ; it is better to start in a corner...
> # (i.e. no need to use X as starting value)
> xs <- xsample(E = E_coli$A, F = E_coli$B, G = E_coli$G, H = E_coli$H,
+ iter = 2000, out = 500, jmp = 50, type = "mirror",
+ x0 = X)$X
Warning in lsei(E = E, F = F, G = G, H = H) :
LSEI error: inequalities contradictory
Warning in varranges(E, F, G, H, EqA = G) :
cannot proceed: problem not solvable at requested tolerance 1e-08
Warning in lsei(E = E, F = F, G = G, H = H) :
LSEI error: inequalities contradictory
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc