I am running rubias on a dataset using mitochondrial DNA and have coded as instructed to specify a haploid system. The program runs perfectly with a set of references from 23 collections (for this trial I am running as if they each belonged to its own repunit), when run against a mixture containing information from 20 collections (to evaluate contributions to all mixtures from each of the 23 reference populations).
I would like to now run rubias with a pi_prior, specifying the different population sizes of each reference. I assumed this could be easily accomplished by setting up a tibble with two columns: collection (names of my 23 collections as used in my data file) and pi_param (the abundance values I mentioned earlier):
tibble [23 x 2] (S3: tbl_df/tbl/data.frame)
$ collection: chr [1:23] "AG" "BBL" "BBW" "BRB" ...
$ pi_param : int [1:23] 203 1504 150 1345 1222 20 25 130 100 15 ...
However, when I try to run infer_mixture, I get this error :
Scoring locus CR as haploid
Scoring locus CR as haploid
Collating data; compiling reference allele frequencies, etc. time: 0.11 seconds
Computing reference locus specific means and variances for computing mixture z-scores time: 0.01 seconds
Working on mixture collection: ASCfg with 22 individuals
Joining, by = "collection"
Error in vec_assign():
! Can't convert from replace$pi_param to data$pi_param due to loss of precision.
- Locations: 1
Run rlang::last_error() to see where the error occurred.
I would be grateful for any help in solving my issue.
best, Alberto
I am running rubias on a dataset using mitochondrial DNA and have coded as instructed to specify a haploid system. The program runs perfectly with a set of references from 23 collections (for this trial I am running as if they each belonged to its own repunit), when run against a mixture containing information from 20 collections (to evaluate contributions to all mixtures from each of the 23 reference populations).
I would like to now run rubias with a pi_prior, specifying the different population sizes of each reference. I assumed this could be easily accomplished by setting up a tibble with two columns: collection (names of my 23 collections as used in my data file) and pi_param (the abundance values I mentioned earlier):
tibble [23 x 2] (S3: tbl_df/tbl/data.frame)
$ collection: chr [1:23] "AG" "BBL" "BBW" "BRB" ...
$ pi_param : int [1:23] 203 1504 150 1345 1222 20 25 130 100 15 ...
However, when I try to run infer_mixture, I get this error :
Scoring locus CR as haploid
Scoring locus CR as haploid
Collating data; compiling reference allele frequencies, etc. time: 0.11 seconds
Computing reference locus specific means and variances for computing mixture z-scores time: 0.01 seconds
Working on mixture collection: ASCfg with 22 individuals
Joining, by = "collection"
Error in
vec_assign():! Can't convert from
replace$pi_paramtodata$pi_paramdue to loss of precision.Run
rlang::last_error()to see where the error occurred.I would be grateful for any help in solving my issue.
best, Alberto