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A month ago
(412 views)

Hi,

I am trying some Bayesian bootstrapping and would like to do it in JMP Pro. One of the prior distibutions I would like to use is Dirichlet. I was thinking of using the column data type Expression to hold vectors of the weights and using the Compress to Label Addin to index the unique values in the column so I know which row/column of the vector contains the correct weight to apply.

I can generate Dirichlet vectors using the R connector (below) but would like to do it in a formula column so I can take advantage of the Bootstrap feature in Pro. Any suggestions?

```
Names Default To Here( 1 );
R Init();
//input list, outputlist, Rcode
rc = R Execute(
{nSamples,pollResult},
{x},
"
library (DirichletReg)
x <- rdirichlet(nSamples, c(728,584,138))"
);
Wait( 3 );
//get dataframe from R
resultMatrix = R Get(x);
//close connection
R Term();
//export results
dt = As Table( resultMatrix );
```

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A month ago
(649 views)

Solution

Hi Stephen,

You can paste the code below into a column formula, and it should sample from a Dirichlet. I'd recommend that you convince yourself that it's accurate before using it, since I didn't test it. I wrote it based off of https://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generation

```
alpha = [1 2 3 4 5];
gams = Random Gamma(alpha)`;
gams/sum(gams); // values sampled from dirichlet with concentration parameters alpha
```

Milo

3 REPLIES

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A month ago
(398 views)

The Dirichlet distribution is not available directly in JMP but you do not need to call R in this case. You can make a column formula in which you use the functions that are available in JMP. See, for example, "What is an intuitive explanation of the Dirichlet distribution?" for more information.

Learn it once, use it forever!

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A month ago
(650 views)

Hi Stephen,

You can paste the code below into a column formula, and it should sample from a Dirichlet. I'd recommend that you convince yourself that it's accurate before using it, since I didn't test it. I wrote it based off of https://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generation

```
alpha = [1 2 3 4 5];
gams = Random Gamma(alpha)`;
gams/sum(gams); // values sampled from dirichlet with concentration parameters alpha
```

Milo

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4 weeks ago
(307 views)