The compositional data sets have few observations: 4 to 7 rows each, but there are 5 parts (columns) for each row.
I tried to use the robCompositions function pcaCoDa(). There was an error and warning generated: ( winters.biplot <- pcaCoDa(winters.coda) ) Error in princomp.default(xilr, covmat = cv, cor = FALSE) : covariance matrix is not non-negative definite In addition: Warning message: In covMcd(xilr, cor = FALSE) : n < 2 * p, i.e., possibly too small sample size The matrix for winters.code has the counts: filter gather graze predate shred 1 3 27 3 11 1 2 3 28 3 13 2 3 3 43 7 15 1 4 4 54 6 24 3 5 3 26 4 22 5 6 1 39 2 18 2 Same results with the data file winters.acomp: filter gather graze predate shred [1,] 0.06666667 0.6000000 0.06666667 0.2444444 0.02222222 [2,] 0.06122449 0.5714286 0.06122449 0.2653061 0.04081633 [3,] 0.04347826 0.6231884 0.10144928 0.2173913 0.01449275 [4,] 0.04395604 0.5934066 0.06593407 0.2637363 0.03296703 [5,] 0.05000000 0.4333333 0.06666667 0.3666667 0.08333333 [6,] 0.01612903 0.6290323 0.03225806 0.2903226 0.03225806 attr(,"class") [1] "acomp" Is there a minimum number of observations for PCA or was I using the incorrect data format? Rich _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology