I have several problems using the omicsCompAnalysis of STATegRa:

When computing the common components with selectCommonComps, the common values are 0. One reason might be that when plotting the ssq the XY or YX component is usually much higher than the variance of X and Y. Looking at the pratios plot, for the first component the SSQ ratios between each block and estimator for one block is close to 6 while the other is near 0. In the next commponents the ratio for each block is similar but close to 0. However when using PCA.selection, I get 7 commononents at least.

>PCA.selection(data1, fac.sel = "single%", varthreshold = 0.03)$numComps
## [1] 7
>PCA.selection(data2, fac.sel = "single%", varthreshold = 0.03)$numComps
## [1] 8

Using modelSelection agrees with the two previous functions:

> modelSelection(list(ES1, ES2), Rmax = 7, fac.sel = "single%",
+                varthreshold = 0.03)
$common
[1] 0

$dist
[1] 7 8

When using the function (with 2 common component analysis) in DISCOSCA method I get an error:

> oCA <- omicsCompAnalysis(list(ES1, ES2), 
+                   Names = c("Intestinal", "Stools"),
+                   method = "DISCOSCA",
+                   Rcommon = 2,
+                   Rspecific = 2)
[1] "The following features are eliminated because are constant: 6 \n OTU_1998, OTU_3076, OTU_437, OTU_499, OTU_68, OTU_75"
[1] "The following features are eliminated because are constant: 2 \n OTU_422, OTU_99"
Error in matrix(1, nrow = nrow(block1), ncol = d2) : 
  invalid 'ncol' value (too large or NA)

The ES1 and ES2 object where created using createOmicsExpressionSet with the same data as in data1 and data2.

When I tried to do the analysis with the same data omicsCompAnalysis(list(ES1, ES1), ... I got the same error:

Error in matrix(1, nrow = nrow(block1), ncol = d2) : 
  invalid 'ncol' value (too large or NA)
>traceback()
7: matrix(1, nrow = nrow(block1), ncol = d2)
6: cbind(matrix(0, nrow = nrow(block1), ncol = d1), matrix(1, nrow = nrow(block1), 
       ncol = d2), matrix(0, nrow = nrow(block1), ncol = R - d1 - 
       d2))
5: rbind(cbind(matrix(0, nrow = nrow(block1), ncol = d1), matrix(1, 
       nrow = nrow(block1), ncol = d2), matrix(0, nrow = nrow(block1), 
       ncol = R - d1 - d2)), cbind(matrix(1, nrow = nrow(block2), 
       ncol = d1), matrix(0, nrow = nrow(block2), ncol = d2), matrix(0, 
       nrow = nrow(block2), ncol = R - d1 - d2)))
4: DISCO_SCA(d1 = Rspecific[1], d2 = Rspecific[2], common = Rcommon, 
       block1 = Data[[1]], block2 = Data[[2]], maxiter = maxIter, 
       convergence = convThres)
3: DISCO_SCA(d1 = Rspecific[1], d2 = Rspecific[2], common = Rcommon, 
       block1 = Data[[1]], block2 = Data[[2]], maxiter = maxIter, 
       convergence = convThres)
2: omicsCompAnalysis(list(ES1, ES2), Names = c("Intestinal", 
       "Stools"), method = "DISCOSCA", Rcommon = cc$common, Rspecific = 2)
1: omicsCompAnalysis(list(ES1, ES2), Names = c("Intestinal", 
       "Stools"), method = "DISCOSCA", Rcommon = cc$common, Rspecific = 2)

Is this error telling me there isn't a common component? How can I use DISCOSA?

PS: It seems that instead of using message it uses printto notify the users.

R version 3.4.2 (2017-09-28)
Platform: i686-pc-linux-gnu (32-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=es_ES.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=es_ES.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Biobase_2.38.0      BiocGenerics_0.24.0 STATegRa_1.12.0     RGCCA_2.1.2         ggplot2_2.2.1      

loaded via a namespace (and not attached):
 [1] codetools_0.2-15      preprocessCore_1.40.0 digest_0.6.12         scales_0.5.0          grid_3.4.2            bitops_1.0-6          gdata_2.18.0         
 [8] zlibbioc_1.24.0       munsell_0.4.3         compiler_3.4.2        tibble_1.3.4          affy_1.56.0           lattice_0.20-35       labeling_0.3         
[15] foreach_1.4.3         iterators_1.0.8       KernSmooth_2.23-15    MASS_7.3-47           BiocInstaller_1.28.0  plyr_1.8.4            gplots_3.0.1         
[22] caTools_1.17.1        gtable_0.2.0          rlang_0.1.4           edgeR_3.20.1          colorspace_1.3-2      calibrate_1.7.2       tools_3.4.2          
[29] affyio_1.48.0         locfit_1.5-9.1        gridExtra_2.3         limma_3.34.0          lazyeval_0.2.1        gtools_3.5.0          Rcpp_0.12.13