acovamcmc: Gibbs sampler for one-way mixed-effects ANOVA and ANCOVA...

In GibbsACOV: Gibbs Sampler for One-Way Mixed-Effects ANOVA and ANCOVA Models

Description

Gibbs sampler for one-way mixed-effects ANOVA and ANCOVA models using flat priors.

Usage

  acovamcmc(Y, trt, X, nochn, numIter, initval, credint = 0.95, Rthresh = 1.1)

Arguments

Y	Vector of reponses of n subjects
trt	Vector of categorical factor levels for n subjects
X	Design matrix with dimension (n x p) where p is the number of continuous predictors (for ANOVA, p = 1 to include grand mean)
nochn	Number of chains to test convergence of the Gibbs sampler
numIter	Number of iterations in the Gibbs sampler
initval	Matrix of initial values for Gibbs sampler with dimension (nochn, (p + nlevels(trt) + 2))
credint	Coverage probability for parameter credible intervals
Rthresh	Gelman-Rubin diagnostic for test of convergence

Value

S3 acovamcmc object; a list consisting of

beta	values of regression coefficients for each iteration
sig2a	values of mixed-effect variance for each iteration
sig2e	values of error variance for each iteration
Credible_Interval	lower bound, point estimate, and upper bound for parameters
Credible_Interval_Coverage	coverage percentage for credible intervals
Convergence_Diag	status of Gibbs sampler convergence using threshold set for Gelman and Rubin's diagnostic
Gelman_Rubin_Threshold	threshold set for Gelman and Rubin's diagnostic
Iterations	number of iterations of Gibbs sampler
Run_Time	total elapsed seconds

References

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.

Examples

## Not run: 
# ANCOVA with 2 continuous predictors and 5 factor levels
  data(corn)
  init1 <- c(rep(0,7), 1, 1)
  init2 <- c(rnorm(7), rgamma(2,2,1))
  init3 <- c(rnorm(7), rgamma(2,2,1))
  init4 <- c(rnorm(7), rgamma(2,2,1))
  initval <- rbind(init1, init2, init3, init4)
  acovamcmc(corn$yield, corn$variety, cbind((corn$nitrogen)^2, corn$nitrogen), 4, 10000 , initval)
# ANOVA with grand mean parameterization and 12 factor levels
  data(csection)
  init1 <- c(rep(0,13), 1, 1)
  init2 <- c(rnorm(13), rgamma(2,2,1))
  init3 <- c(rnorm(13), rgamma(2,2,1))
  init4 <- c(rnorm(13), rgamma(2,2,1))
  initval <- rbind(init1, init2, init3, init4)
  Y = log(csection$rate / (1-csection$rate))
  acovamcmc(Y, factor(csection$hospital), matrix(1,length(csection$hospital),1), 4, 10000, initval)

## End(Not run)

GibbsACOV documentation built on May 2, 2019, 12:36 p.m.