MRwarp: Main function to perform multiresolution warping for...
In MRwarping: Multiresolution time warping for functional data.

Description Usage Arguments Value Author(s) References Examples

The Bayesian procedure starts with one warplet in the model and uses the posterior distributions as priors for a more extended model with one more warplet. The model is built with adding one warplet at a time and allows for amplitude variations.

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MRwarp(Xdata, Ydata, chain = 400, thin = 10, burnin = 200, kernel.s,
 	components = 1, selection = "FIXED", shr = 0.3, outputfit = 1, 
	alpha = 0.1)

`Xdata`	N by T matrix containing the T x-coordinates or time points of the N curve observations. Each row corresponds to a particular subject. No default.
`Ydata`	N by T matrix containing the T y-coordinates or response values of the N curve observations. Each row corresponds to a particular subject. No default.
`chain`	The total number of MCMC iterations (default=400).
`thin`	The thinning factor of the MCMC algorithm (default=10).
`burnin`	The number of MCMC iterations that are discarded (default=200).
`kernel.s`	Vector containing the starting values for the kernel parameters. No default.
`components`	The number of warping components in the final model (default=1). The value is ignored when selection="STEP".
`selection`	Whether we want to estimate a fixed number of warplets ("FIXED"), or evaluate the warping procedure after each component ("STEP") (default= "FIXED").
`shr`	Determines the variance of the prior on the warplet intensities and shifts (default = 0.3).
`outputfit`	1 if the warped curves should be plotted after each estimated model, 0 otherwise (default=1).
`alpha`	The significance level to be used in the model selection procedure (default=0.1).

`last`	List of output values for the last fitted model.
`previous`	List of output values for the one but last fitted model.
`shift`	Component of the output list. A MCMC chain of the estimated horizontal shift for curves 1 to N.
`warping`	Component of the output list that is itself a list containing four quantities: lower, A, upper and Intensities.
`lower`	Component of warping. Adaptive MCMC chains of the estimated warping lower bounds (w_{l,1}, …, w_{l,Q-1}).
`A`	Component of warping. Adaptive MCMC chains of the estimated warping centers (a_1, …, a_{Q-1}).
`upper`	Component of warping. AMCMC chains of the estimated warping upper bounds (w_{u,1}, …, w_{u,Q-1}).
`Intensities`	Component of warping. Adaptive MCMC chains of the estimated warping intensities, first all N values for warplet 1,..., all N values for warplet Q.
`kernels`	Component of the output list. Adaptive MCMC chains of the estimated kernel lower bounds, centers and upper bounds.
`error.variance`	Component of the output list. The estimated value of the error variance.
`max.post.dens`	Component of the output list. The row in the parameter chain vectors/matrices corresponding to the highest posterior pseudo-log-likelihood.

L. Slaets, G. Claeskens, B.W. Silverman.

See the papers: Slaets, Claeskens and Silverman (2010). Warping functional data in R and C via a Bayesian Multiresolution approach. Journal of Statistical Software, 55(3), 1-22,
URL http://www.jstatsoft.org/v55/i03/.

Claeskens, Silverman and Slaets (2010). A multiresolution approach to time warping achievec by a Bayesian prior-posterior transfer fitting strategy. Journal of the Royal Statistical Society, Series B, 72(5), 673-694.

data(TICdata)
TIC = as.matrix(TICdata)

index = 1:200*2-1
TICy = t(matrix(index,200,11))
x = 1:400
for (i in 1:11)
	{
	TIC.sm = spm(TIC[i,]~f(x))
	TICy[i,] = TIC.sm$fit$fitted[index]
	}
TICx = t(matrix(index,200,11))
kernel.s = c(70,100,130,270,285,300)

## Not run: 
output = MRwarp(Xdata=TICx,Ydata=TICy,chain=100,thin=5,burnin=50,kernel.s,
components=1,selection="FIXED",shr=0.3,outputfit=1,alpha=0.1)

## End(Not run)