# Fit a multivariate normal distribution to x and y data using jags This function contains and defines the jags model script used to fit a bivariate normal distribution to a vector of x and y data. Although not intended for direct calling by users, it presents a quick way to fit a model to a single group of data. Advanced users should be able to manipulate the contained jags model to fit more complex models using different likelihoods, such as multivariate lognormal distributions, multivariate gamma distributions etc... ```r fitEllipse(x, y, parms, priors, id = NULL) ``` ## Arguments - `x`: a vector of data representing the x-axis - `y`: a vector of data representing the y-axis - `parms`: a list containing four items providing details of the `rjags::rjags()` run to be sampled. * `n.iter` The number of iterations to sample * `n.burnin` The number of iterations to discard as a burnin from the start of sampling. * `n.thin` The number of samples to thin by. * `n.chains` The number of chains to fit. - `priors`: a list of three items specifying the priors to be passed to the jags model. * `R` The scaling vector for the diagonal of Inverse Wishart distribution prior on the covariance matrix Sigma. Typically set to a 2x2 matrix `matrix(c(1, 0, 0, 1), 2, 2)`. * `k` The degrees of freedom of the Inverse Wishart distribution for the covariance matrix Sigma. Typically set to the dimensionality of Sigma, which in this bivariate case is 2. * `tau` The precision on the normal prior on the means mu. - `id`: a character string to prepend to the raw saved jags model output. This is typically passed on from the calling function `siberMVN()` and identifies the community and group with an integer labelling system. Defaults to NULL which will prevent the output object being saved even if `parms$save.output` is set to `TRUE`. The file itself will be saved to the user-specified location via `parms$save.dir`. ## Returns A mcmc.list object of posterior samples created by jags. ## Examples ```r x <- stats::rnorm(50) y <- stats::rnorm(50) parms <- list() parms$n.iter <- 2 * 10^3 parms$n.burnin <- 500 parms$n.thin <- 2 parms$n.chains <- 2 priors <- list() priors$R <- 1 * diag(2) priors$k <- 2 priors$tau.mu <- 1.0E-3 fitEllipse(x, y, parms, priors) ```

fitEllipse function