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R: Main function to perform multiresolution warping for...

MRwarp

R Documentation

Main function to perform multiresolution warping for functional data in a Bayesian way.

Description

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.

Usage

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

Arguments

`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).

Value

`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.

Author(s)

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

References

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.

Examples

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MRwarping)
Loading required package: boa
Loading required package: SemiPar
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MRwarping/MRwarp.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MRwarp
> ### Title: Main function to perform multiresolution warping for functional
> ###   data in a Bayesian way.
> ### Aliases: MRwarp
> 
> ### ** Examples
> 
> 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: 
> ##D output = MRwarp(Xdata=TICx,Ydata=TICy,chain=100,thin=5,burnin=50,kernel.s,
> ##D components=1,selection="FIXED",shr=0.3,outputfit=1,alpha=0.1)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>