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You must be a registered user to use the IMDb rating plugin. User Polls Top Hat! Edit Cast Complete credited cast: Jane Russell Dorothy Shaw Marilyn Monroe Lorelei Lee Charles Coburn Ernie Malone Tommy Noonan Gus Esmond Jr.

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Edit Did You Know? Trivia In her very last interview 10 years after making "Gentlemen Prefer Blondes" Marilyn Monroe recalls the lack of respect studio execs had for her, but makes a point of mentioning co-star, Jane Russell : "I remember when I got the part in 'Gentlemen Prefer Blondes.

She, by the way, was quite wonderful to me. Goofs In the later part of the movie when both Marilyn and Jane are talking, Marilyn has bright red lipstick and then a few seconds later it was a faint pink as if it didn't get colorized.

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The band actually messes-up the song because they are too busy looking at the girls. Was this review helpful to you? Yes No Report this. 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.

Now, we can simulate with that and see what the resulting proportion of fish from each collection is:. Now observe the average proportions of the collections and repunits that were simulated, and the average fraction, within reporting units of each of the collection.

Furthermore, when people asked for this feature it made it clear that Eric had to provide a way to simulate multiple different scenarios without re-processing the reference data set each time.

These can be named lists, if desired. We will let the collections within them just be drawn from a dirichlet distribution with parameter 10 so, pretty close to equal proportions.

So, to do this, we make a list of data frames with the proportions. Doing so takes a little bit longer, computationally, because there is a good deal of simulation involved:.

And now we can compare the estimates, showing here the 10 most prevalent repunits, in the rec1 fishery:. Skip to content.

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Indica que te gusta en Facebook para ver historias similares. 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. Now, we can simulate with that and see what the resulting proportion of fish from each collection is:.

Now observe the average proportions of the collections and repunits that were simulated, and the average fraction, within reporting units of each of the collection.

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