Description Usage Arguments Details Value References Examples
Method-of-Moments (MoM) estimators of the Dirichlet-multinomial parameters: taxa proportions and overdispersion.
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data |
A matrix of taxonomic counts(columns) for each sample(rows). |
Given a set of taxa-count vectors ≤ft\{\textbf{x}_{i},…, \textbf{x}_{P} \right\}, the methods of moments (MoM) estimator of the set of parameters θ and ≤ft\{π_{j} \right\}_{j=1}^K is given as follows (Mosimann, 1962; Tvedebrink, 2010):
\hat{π}_{j}=\frac{∑_{i=1}^P x_{ij}}{∑_{i=1}^P N_{i}},
and
\hat{θ} = ∑_{j=1}^K \frac{S_{j}-G_{j}}{∑_{j=1}^{K}≤ft ( S_{j}+≤ft ( N_{c}-1 \right )G_{j} \right )},
where N_{c}=≤ft( P -1 \right)^{-1} ≤ft(∑_{i=1}^P N_{i}-≤ft (∑_{i=1}^P N_{i} \right )^{-1} ∑_{i=1}^P N_{i}^2\right), and S_{j}=≤ft( P -1 \right)^{-1} ∑_{i=1}^P N_{i} ≤ft ( \hat{π}_{ij} -\hat{π}_{j} \right )^{2}, and G_{j}=≤ft( ∑_{i=1}^P ≤ft (N_i-1 \right ) \right)^{-1} ∑_{i=1}^P N_{i} \hat{π}_{ij} ≤ft (1- \hat{π}_{ij}\right) with \hat{π}_{ij}=\frac{x_{ij}}{N_{i}}.
A list providing the MoM estimator for overdispersion, the MoM estimator of the RAD-probability mean vector, and the corresponding loglikelihood value for the given data set and estimated parameters.
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.
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Loading required package: dirmult
Attaching package: 'HMP'
The following object is masked from 'package:dirmult':
weirMoM
$loglik
[1] -3237.232
$gamma
Taxa 1 Taxa 2 Taxa 3 Taxa 4 Taxa 5 Taxa 6 Taxa 7
13.4057765 4.8653659 3.3937428 2.5327106 1.9077238 1.5488983 1.2693338
Taxa 8 Taxa 9 Taxa 10 Taxa 11 Taxa 12 Taxa 13 Taxa 14
1.0673755 0.8849429 0.7353565 0.6178715 0.5285995 0.4326259 0.3748762
Taxa 15 Taxa 16 Taxa 17 Taxa 18 Taxa 19 Taxa 20 Taxa 21
0.3211807 0.2730284 0.2257035 0.2014619 0.1885551 0.1696913 1.8453410
$pi
Taxa 1 Taxa 2 Taxa 3 Taxa 4 Taxa 5 Taxa 6
0.364384825 0.132246385 0.092245935 0.068842063 0.051854184 0.042100884
Taxa 7 Taxa 8 Taxa 9 Taxa 10 Taxa 11 Taxa 12
0.034501990 0.029012526 0.024053793 0.019987856 0.016794477 0.014367958
Taxa 13 Taxa 14 Taxa 15 Taxa 16 Taxa 17 Taxa 18
0.011759282 0.010189579 0.008730069 0.007421234 0.006134887 0.005475971
Taxa 19 Taxa 20 Taxa 21
0.005125149 0.004612409 0.050158545
$theta
[1] 0.02646191
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