dewarp2d: Fit Bayesian 2D image dewarping models

In oslerinhealth/spotgear: Subset Profiling and Organizing Tools for Gel Electrophoresis Autoradiography in R

Description

Fit Bayesian 2D image dewarping models

Usage

dewarp2d(data_dewarp, mcmc_options, L = 100)

Arguments

data_dewarp	Data frame with columns: gel_ID (true id), lane_ID (1 to say 20, 1 will be removed), band_ID (counts within each lane), Y (t-scale location of peaks), peak_bin_index (index of the bin corresponding to the peaks; requires common binning across images), lane_ID_stacked (cummulative lane number upon stacking gels one-by-one; mainly for plotting).
mcmc_options	A list of Markov chain Monte Carlo (MCMC) options.
L	number of landmarks; default to 100. debugstatus Logical - whether to pause WinBUGS after it finishes model fitting; n.chains Number of MCMC chains; n.burnin Number of burn-in samples; n.thin To keep every other n.thin samples after burn-in period; result.folder Path to folder storing the results; bugsmodel.dir Path to WinBUGS model files;

Details

This function prepares data, specifies hyperparameters in priors, initializes the posterior sampling chain, writes the model file (for JAGS syntax), and fits the model. Features:

If running JAGS on windows, please go to control panel to add the directory to jags into ENVIRONMENTAL VARIABLE!

Value

BUGS fit results.

oslerinhealth/spotgear documentation built on May 25, 2021, 10:38 p.m.