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

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

	
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.