diffractometry (version 0.1-10)

pkdecomp: Decomposition of peaks for the whole data set

Description

Calculates decompositions of peaks for the whole diffractogram

Usage

pkdecomp(baslfit,intnum=0, alpha=0.1, maxiter1=500, maxiter=10000, hmax=5, 
maxsolutions=3,heterosk=TRUE,baselim=c(0.05,5),dispers=1)

Arguments

baslfit

Output of baselinefit

intnum

Vector of numbers of intervals. If intnum = 0, all intervals are used

alpha

Test level for residual criterion

maxiter1

Number of attempts to fit a model with 1 component

maxiter

Number of attempts to fit a model with k > 1 components

hmax

Maximum number of components

maxsolutions

Number of solutions with k components

heterosk

If TRUE, the estimate of noise level given in baslfit is used (default); otherwise noise level is taken to be proportional to signal height

baselim

Limits for changes in the baseline estimate; first component is given in percent of the baseline height, second in counts/2theta

dispers

Additional dispersion factor; not used if heterosk==T

Value

A vector of lists as given by pkdecompint

Details

Calls pkdecompint to decompose the peaks found by baselinefit into Pearson Type VII kernels. For every interval, first one kernel is tried. The number of kernels is increased until either a solution accepted by the residual criterion is found or the maximum number of kernels hmax is reached. After a solution is accepted, for maxsolutions greater than 1, further decompositions with the same number of kernels can be produced.

References

P.L. Davies, U. Gather, M. Meise, D. Mergel, T. Mildenberger (2008): "Residual based localization and quantification of peaks in x-ray diffractograms", Annals of Applied Statistics, Vol. 2, No. 3, 861-886.. http://www.statistik.tu-dortmund.de/fileadmin/user_upload/Lehrstuehle/MSind/Publikationen/2008/2008_-_Davies_Gather_Meise_Mergel_Mildenberger_-_Residual_based_localization_and_quantification_of_peaks_in_x-ray_diffractograms.pdf

T. Bernholt and T. Hofmeister (2006): "An algorithm for a generalized maximum subsequence problem", in: J. Correa, A. Hevia, M. Kiwi (editors), "Latin 2006: Theoretical Informatics", volume 3887 of Lecture notes in Computer Science, pages 178-189, Berlin, Heidelberg. Springer Verlag

See Also

diffractogram, baselinefit, pkdecompint

Examples

# NOT RUN {
## Decomposition of data in peak interval into two components

set.seed(0)

par(mfrow=c(2,1))

data(indiumoxide)
indox<-indiumoxide[1901:2400,]
base<-baselinefit(indox)

ind<-c(base$indlsep[1],base$indrsep[1])

plot(indox[ind[1]:ind[2],1], base$baseline$peaks[ind[1]:ind[2]],xlab="",ylab="")

pks<-pkdecomp(base,intnum=1,maxsolutions=1,maxiter1=50,maxiter=250)

lines(indox[ind[1]:ind[2],1],pks[[2]]$fit,col="red")
plot(indox[ind[1]:ind[2],1],pks[[2]]$fitpk[1,],ylim=c(0,1800),type="l",xlab="",ylab="")
lines(indox[ind[1]:ind[2],1],pks[[2]]$fitpk[2,])


# }