Description Usage Arguments Details Value References Examples
View source: R/Dirichlet.multinomial.R
Random generation of Dirichlet-Multinomial samples.
1 | Dirichlet.multinomial(Nrs, shape)
|
Nrs |
A vector specifying the number of reads or sequence depth for each sample. |
shape |
A vector of Dirichlet parameters for each taxa. |
The Dirichlet-Multinomial distribution is given by (Mosimann, J. E. (1962); Tvedebrink, T. (2010)),
\textbf{P}≤ft ({\textbf{X}_i}=x_{i};≤ft \{ π_j \right \},θ\right )=\frac{N_{i}!}{x_{i1} !,…,x_{iK} !}\frac{∏_{j=1}^K ∏_{r=1}^{x_{ij}} ≤ft \{ π_j ≤ft ( 1-θ \right )+≤ft ( r-1 \right )θ\right \}}{∏_{r=1}^{N_i}≤ft ( 1-θ\right )+≤ft ( r-1 \right) θ}
where \textbf{x}_{i}= ≤ft [ x_{i1}, …, x_{iK} \right ] is the random vector formed by K taxa (features) counts (RAD vector), N_{i}= ∑_{j=1}^K x_{ij} is the total number of reads (sequence depth), ≤ft\{ π_j \right\} are the mean of taxa-proportions (RAD-probability mean), and θ is the overdispersion parameter.
Note: Though the test statistic supports an unequal number of reads across samples, the performance has not yet been fully tested.
A data matrix of taxa counts where the rows are samples and columns are the taxa.
Mosimann, J. E. (1962). On the compound multinomial distribution, the multivariate β-distribution, and correlations among proportions. Biometrika 49, 65-82.
Tvedebrink, T. (2010). Overdispersion in allelic counts and theta-correction in forensic genetics. Theor Popul Biol 78, 200-210.
1 2 3 4 5 6 7 8 9 10 11 | data(saliva)
### Generate a the number of reads per sample
### The first number is the number of reads and the second is the number of subjects
nrs <- rep(15000, 20)
### Get gamma from the dirichlet-multinomial parameters
shape <- dirmult(saliva)$gamma
dmData <- Dirichlet.multinomial(nrs, shape)
dmData[1:5, 1:5]
|
Loading required package: dirmult
Attaching package: 'HMP'
The following object is masked from 'package:dirmult':
weirMoM
Iteration 1: Log-likelihood value: -1426219.55743915
Iteration 2: Log-likelihood value: -1426159.69392069
Iteration 3: Log-likelihood value: -1426137.57581644
Iteration 4: Log-likelihood value: -1426134.36208055
Iteration 5: Log-likelihood value: -1426134.28420196
Iteration 6: Log-likelihood value: -1426134.28414898
Taxa 1 Taxa 2 Taxa 3 Taxa 4 Taxa 5
Sample 1 2720 1658 1347 1875 510
Sample 2 2996 2062 1515 1389 856
Sample 3 3358 2525 1877 1272 972
Sample 4 3024 1866 1676 1168 926
Sample 5 2609 2113 1385 1455 1057
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