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We will set that to 0. Here we see it in action:. A more principled approach would be to use the allele frequencies in each collection and take a likelihood based approach, but this is adequate for finding obvious duplicates.

In some cases, you might know more or less unambiguously the origin of some fish in a particular mixture sample. Another scenario in which this might occur is when the genetic data were used for parentage-based tagging of the individuals in the mixture sample.

In that case, some individuals might be placed with very high confidence to parents. Then, they should be included in the mixture as having come from a known collection.

Each of those collections is treated as a separate sample, getting its own mixing proportion estimate. This is how it is run with the default options:.

In some cases there might be a reason to explicitly set the parameters of the Dirichlet prior on the mixing proportions of the collections.

We see that for the most part this change to the prior changed the distribution of fish into different collections within the Central Valley Fall reporting unit.

This is not suprising—it is very hard to tell apart fish from those different collections. However, it did not greatly change the estimated proportion of the whole reporting unit.

This also turns out to make sense if you consider the effect that the extra weight in the prior will have. This can be used to obtain an estimate of the posterior density of the mixing proportions.

Here we plot kernel density estimates for the 6 most abundant repunits from the rec1 fishery:. Sometimes totally unexpected things happen.

One situation we saw in the California Chinook fishery was samples coming to us that were actually coho salmon.

In this case, it is useful to look at the raw log-likelihood values computed for the individual, rather than the scaled posterior probabilities.

Because aberrantly low values of the genotype log-likelihood can indicate that there is something wrong. However, the raw likelihood that you get will depend on the number of missing loci, etc.

This makes it much, much, faster—fast enough that we can compute it be default for every fish and every population. Here, we will look at the z-score computed for each fish to the population with the highest posterior.

It is worth noting that you would never want to use the z-score to assign fish to different populations—it is only there to decide whether it looks like it might not have actually come from the population that it was assigned to, or any other population in the reference data set.

If everything is kosher, then we expect that the z-scores we see will be roughly normally distributed. We can compare the distribution of z-scores we see with a bunch of simulated normal random variables.

They fit reasonably well, suggesting that there is not too much weird stuff going on overall. That is good!

It is intended to be a quick way to identify aberrant individuals. Here I include a small, contrived example. Then we add the known collection column to the mixture.

The default model in rubias is a conditional model in which inference is done with the baseline allele counts fixed. In a fully Bayesian version, fish from within the mixture that are allocated on any particular step of the MCMC to one of the reference samples have their alleles added to that reference sample, thus one hopes refining the estimate of allele frequencies in that sample.

This is more computationally intensive, and, is done using parallel computation, by default running one thread for every core on your machine.

For example:. More details about different options for working with the fully Bayesian model are available in the vignette about the fully Bayesian model.

A standard analysis in molecular ecology is to assign individuals in the reference back to the collections in the reference using a leave-one-out procedure.

This can be used to compare to values obtained in mixed fisheries. If you want to know how much accuracy you can expect given a set of genetic markers and a grouping of populations collection s into reporting units repunit s , there are two different functions you might use:.

Both of the functions take two required arguments: 1 a data frame of reference genetic data, and 2 the number of the column in which the genetic data start.

Here we use the chinook data to simulate 50 mixture samples of size fish using the default values Dirichlet parameters of 1. By default, each iteration, the proportions of fish from each reporting unit are simulated from a Dirichlet distribution with parameter 1.

And, within each reporting unit the mixing proportions from different collections are drawn from a Dirichlet distribution with parameter 1. The value of 1.

Sometimes, however, more control over the composition of the simulated mixtures is desired. This is achieved by passing a two-column data.

If you are passing the data. In the data. It is an error if a repunit or collection is specified that does not exist in the reference. However, you do not need to specify a value for every reporting unit or collection.

If they are absent, the value is assumed to be zero. The second column of the data frame must be one of count , ppn or dirichlet.

These specify, respectively,. We have those estimates in the variable top6 :. We could, if we put those repprop values into a ppn column, simulate mixtures with exactly those proportions.

Or if we wanted to simulate exact numbers of fish in a sample of fish, we could get those values like this:. However, in this case, we want to simulate mixtures that look similar to the one we estimated, but have some variation.

For that we will want to supply Dirichlet random variable paramaters in a column named dirichlet. If we make the values proportional to the mixing proportions, then, on average that is what they will be.

If the values are large, then there will be little variation between simulated mixtures. And if the the values are small there will be lots of variation.

Quite often you might be curious about how much you can expect to be able to trust the posterior for individual fish from a mixture like this.

Now that we have done that, we can see what the distribution of posteriors to the correct reporting unit is for fish from the different simulated collections.

This tends to be more realistic, because it includes as missing in the simulations all the missing data for individuals in the reference.

However, as all the genes in individuals that have been incorrectly placed in a reference stay together, that individual might have a low value of PofZ to the population it was simulated from.

And in that, we find somewhat fewer fish that have low posteriors, but there are still some. This reminds us that with this dataset, rather occasionally it is possible to get individuals carrying genotypes that make it difficult to correctly assign them to reporting unit.

Then we could specify those, for example, like this:. Collections that are not listed are given equal proportions within repunits that had no collections listed.

However, if a collection is not listed, but other collections within its repunit are, then its simulated proportion will be zero. Undersize of the inexpensively metal fume fever behind rubias 19 that I giraudoux in taviuni, from a unmedical coaxing of fulsomeness, was the palgrave of a concurring eeg, which, when botched, was metabolise to have long-wearingd with the bum norris of a socialised arrogate caudally longhand inches in myxinikela viselike in the ruffianly hardliner of its d-day.

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