# fitcomp: Gibbs sampler for parameter estimation In ocomposition: Regression for Rank-Indexed Compositional Data

## Description

The main regression function for compositional rank-index data. For units i = 1, ..., n, the response variable is vector (y_{i1}, ..., y_{in}), where ∑_j y_{ij} = 1 and y_{i1} ≥q y_{i2} ≥q ... ≥q y_{in} for all i and y_{ij} \in [0, 1] for all i and j. The regression model has two parts: a truncated negative binomial model for the count of non-zero components and a set of seemingly unrelated t regressions for the compositions. See References for further details.

## Usage

 1 2 fitcomp(data.v, data.x, n.formula, v.formula, l.bound = 1, n.sample = 100, burn = 0, thin = 1, init = NULL) 

## Arguments

 data.v Matrix of compositional data: rows for units and columns for components. Rows must add up to 1; if not, they are automatically rescaled. NA values turned into 0 automatically. Ordering done automatically. data.x Data frame with covariates, missing values not allowed. n.formula formula for the number of components: e.g.,  ~ x1 + x2 + factor(z). v.formula formula for the size of components: e.g.,  ~ x1 + x2. l.bound lower bound for the negative binomial regression; must be greater or equal to 1; default = 1. n.sample number of samples you want to have after burn-in and thinning; default 100 burn number of burn-in samples; default 0 thin thinning of the MCMC chain; default 1 init initial parameters; not required

## Value

 g samples of gamma coefficients for the multivariate regression model b posterior samples of the coefficients for the negative binomial regression mu hyperparameters for gamma coefficients rho shrinkage hyperparameters for gamma coefficients Sigma posterior samples of the covariance matrix nu degrees of freedom for the Student's t distribution

## References

Rozenas, Arturas (2012) 'A Statistical Model for Party Systems Analysis', Political Analysis, 2(20), p.235-247.

## Examples

 1 2 3 4 5 6 7 8 9  data(data) out <- fitcomp(data$v, data$m, ~ log(m), ~ log(m) + log(n), n.sample = 50) summary(out) # predict distribution of votes in a country with 5-member median district v.hat <- predict(out, data.frame(m=5)) plot(v.hat) 

ocomposition documentation built on May 2, 2019, 3:30 p.m.