I have a dataset of 39 individuals with 11,671 codominant loci. I wanted to calculate genetic distance - Euclidean distance.
I used the following codes,
observed <- dist(genclone_m, method = "euclidean", diag = FALSE, upper=FALSE, p =2)
So, I took a look at the summary info of the calculated distances using summary(observed). From the results, I don't understand why the Euclidean matrix showed "FALSE".
Class: dist
Distance matrix by lower triangle : d21, d22, ..., d2n, d32, ...
Size: 39
Labels: Vatr_M_1.2_fixed Vatr_M_12B Vatr_M_12D Vatr_M_12E Vatr_M_14 Vatr_M_15.2 Vatr_M_17 Vatr_M_18.2 Vatr_M_19 Vatr_M_20 Vatr_M_21 Vatr_M_22D Vatr_M_26 Vatr_M_28 Vatr_M_29 Vatr_M_32 Vatr_M_33 Vatr_M_34 Vatr_M_35 Vatr_M_36D Vatr_M_38 Vatr_M_40 Vatr_M_41A_fixed Vatr_M_41B Vatr_M_41D Vatr_M_41E Vatr_M_41F Vatr_M_41H Vatr_M_42 Vatr_M_44 Vatr_M_46 Vatr_M_51A Vatr_M_51B Vatr_M_52 Vatr_M_53 Vatr_M_54 Vatr_M_57A Vatr_M_57C Vatr_M_58
call: dist(x = genclone_m, method = "euclidean", diag = FALSE, upper = FALSE,
p = 2)
method: euclidean
Euclidean matrix (Gower 1966): FALSE
I also tried calculating the Euclidean distance using bitwise.dist().
observed <- bitwise.dist(genclone_m, missing_match = TRUE, scale_missing = TRUE, euclidean = TRUE, threads = 1L)
summary(observed)
From the result, I see that the method is Provesti even though I used "Euclidean = TRUE" and the Euclidean matrix is FALSE.
Class: dist
Distance matrix by lower triangle : d21, d22, ..., d2n, d32, ...
Size: 39
Labels: Vatr_M_1.2_fixed Vatr_M_12B Vatr_M_12D Vatr_M_12E Vatr_M_14 Vatr_M_15.2 Vatr_M_17 Vatr_M_18.2 Vatr_M_19 Vatr_M_20 Vatr_M_21 Vatr_M_22D Vatr_M_26 Vatr_M_28 Vatr_M_29 Vatr_M_32 Vatr_M_33 Vatr_M_34 Vatr_M_35 Vatr_M_36D Vatr_M_38 Vatr_M_40 Vatr_M_41A_fixed Vatr_M_41B Vatr_M_41D Vatr_M_41E Vatr_M_41F Vatr_M_41H Vatr_M_42 Vatr_M_44 Vatr_M_46 Vatr_M_51A Vatr_M_51B Vatr_M_52 Vatr_M_53 Vatr_M_54 Vatr_M_57A Vatr_M_57C Vatr_M_58
call: prevosti.dist(x = x)
method: Provesti
Euclidean matrix (Gower 1966): FALSE
Happy to hear more suggestions on interpreting the results. Thank you very much.
Regards,
Shan