Hi all!

I am trying to get a Q-matrix from the best snmf() run. This only works for me when there is a single run, if I am setting the repetitions to > 1, however, I am not able to generate a Q matrix from the best run. Can anyone help?

#snmf with 10 repetitions
df <- data.frame(id= c(1,1,1,2,2,2,2,3,3,3),
                 marker1= c(1,1,1,2,2,2,2,3,3,3),
                 marker2 = c(1,1,1,2,2,2,2,3,3,3),
                 marker3 = c(1,1,1,2,2,2,2,3,3,3),
                 marker4 = c(1,1,1,2,2,2,2,3,3,3), 
                 marker5 = c(1,1,1,2,2,2,3,3,3,3),
                 marker6 = c(1,1,1,2,2,2,3,3,3,3),
                 marker7 = c(1,1,1,2,2,2,3,3,3,3),
                 marker8 = c(1,1,1,2,2,2,3,3,3,3),
                 marker9 = c(1,1,1,2,2,2,3,3,3,3))
write.table(df, "df.txt", sep="\t", row.names=F, col.names=T, quote=F)

struct2geno(file = "df.txt", TESS = F, diploid = TRUE, FORMAT = 2,
            extra.row = 1, extra.col = 1, output = "./df.geno")
obj.snmf <- snmf("df.geno", K = 3, entropy=T, repetitions = 10, project = "new")
best <- which.min(cross.entropy(obj.snmf, K = 3))
best_data <- obj.snmf@runs[[best]]
qmatrix <- Q(best_data, K = 3)

#calling qmatrix - this is not what I wanted
qmatrix
function (data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL) 
{
    if (is.object(data) || !is.atomic(data)) 
        data <- as.vector(data)
    .Internal(matrix(data, nrow, ncol, byrow, dimnames, missing(nrow), 
        missing(ncol)))
}
<bytecode: 0x000002180d85b5f0>
<environment: namespace:base>

Now there is only one run and it works.

df <- data.frame(id= c(1,1,1,2,2,2,2,3,3,3),
                 marker1= c(1,1,1,2,2,2,2,3,3,3),
                 marker2 = c(1,1,1,2,2,2,2,3,3,3),
                 marker3 = c(1,1,1,2,2,2,2,3,3,3),
                 marker4 = c(1,1,1,2,2,2,2,3,3,3), 
                 marker5 = c(1,1,1,2,2,2,3,3,3,3),
                 marker6 = c(1,1,1,2,2,2,3,3,3,3),
                 marker7 = c(1,1,1,2,2,2,3,3,3,3),
                 marker8 = c(1,1,1,2,2,2,3,3,3,3),
                 marker9 = c(1,1,1,2,2,2,3,3,3,3))
write.table(df, "df.txt", sep="\t", row.names=F, col.names=T, quote=F)

struct2geno(file = "df.txt", TESS = F, diploid = TRUE, FORMAT = 2,
            extra.row = 1, extra.col = 1, output = "./df.geno")
obj.snmf <- snmf("df.geno", K = 3, project = "new")
qmatrix <- Q(obj.snmf, K = 3)

#Calling qmatrix (desired output)
qmatrix
            V1        V2        V3
[1,] 9.998e-01 9.999e-05 9.999e-05
[2,] 9.998e-01 9.999e-05 9.999e-05
[3,] 9.999e-05 9.998e-01 9.999e-05
[4,] 9.999e-05 9.999e-05 9.998e-01
[5,] 9.999e-05 9.999e-05 9.998e-01

Does anyone know how to generate a Q-matrix from the best run? This would be highly appreciated! And also, as I am new to LEA, would anyone have suggestions on how many repetitions to chose?

Any help would be highly appreceated, thank you!

Best, Janis

And last but not least the sessionInfo:

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=German_Austria.1252  LC_CTYPE=German_Austria.1252    LC_MONETARY=German_Austria.1252 LC_NUMERIC=C                   
[5] LC_TIME=German_Austria.1252    
system code page: 65001

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

other attached packages:
 [1] rgdal_1.5-29       dplyr_1.0.8        RColorBrewer_1.1-2 fields_13.3        viridis_0.6.2      viridisLite_0.4.0  spam_2.8-0        
 [8] mapplots_1.5.1     sp_1.4-6           LEA_3.4.0         

loaded via a namespace (and not attached):
 [1] magrittr_2.0.2   maps_3.4.0       tidyselect_1.1.2 munsell_0.5.0    colorspace_2.0-2 lattice_0.20-45  R6_2.5.1         rlang_1.0.1     
 [9] fansi_1.0.2      tools_4.1.2      dotCall64_1.0-1  grid_4.1.2       gtable_0.3.0     utf8_1.2.2       DBI_1.1.2        cli_3.1.1       
[17] ellipsis_0.3.2   assertthat_0.2.1 tibble_3.1.6     lifecycle_1.0.1  crayon_1.5.1     gridExtra_2.3    purrr_0.3.4      ggplot2_3.3.5   
[25] vctrs_0.3.8      glue_1.6.1       compiler_4.1.2   pillar_1.7.0     generics_0.1.2   scales_1.1.1     pkgconfig_2.0.3 
>