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R

edspk <-
function(eds.exp, kerpk = 1, fitmaxiter = 50, gam = 1.0, scl.factor = 0.1, maxwdth=0.20) {
eds.base <- baselinefit(eds.exp, tau=2.0, gam=gam, scl.factor=scl.factor, maxwdth=maxwdth)
# This loop deals with the output from baselinefit()
# It makes a "melted" dataframe in long form for each
# separated peak for some baseline parameters
eds.pks <- data.frame()
eds.pks.basl <- data.frame()
eds.pks.pmg <- data.frame()
eds.pks.spl <- data.frame()
peaks <- 1:length(eds.base$npks)
for (s in peaks) {
# recorded data in long form by separated peak
eds.pks <- rbind(eds.pks, # column names assigned after loop
data.frame(peak = factor(peaks[s]),
kernel = NA,
eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s], ]))
# the calculated baseline in long form by separated peak
eds.pks.basl <- rbind(eds.pks.basl,
data.frame(peak = factor(peaks[s]),
kernel = NA,
x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]],
y = eds.base$baseline$basisl[eds.base$indlsep[s]:eds.base$indrsep[s]]))
# the taut string estimation in long form by separated peak
eds.pks.pmg <- rbind(eds.pks.pmg,
data.frame(peak = factor(peaks[s]),
kernel = NA,
x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]],
y = eds.base$pmg$fn[eds.base$indlsep[s]:eds.base$indrsep[s]]))
# the weighted smoothed spline in long form by separated peak
eds.pks.spl <- rbind(eds.pks.spl,
data.frame(peak = factor(peaks[s]),
kernel = NA,
x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]],
y = eds.base$spl$reg[eds.base$indlsep[s]:eds.base$indrsep[s]]))
}
# Column names assigned to d.pks
names(eds.pks) <- c("peak", "kernel", "x", "y")
# This loop calls pkdecompint() on each peak separately
# It makes a "melted" dataframe in long form for:
eds.fit <- list() # holds pkdecompint output
eds.fit.fitpk <- data.frame() # contains fitting curves
eds.fit.parpk <- data.frame() # physical parameters by peak and kernel
eds.nobasl <- data.frame() # data with baseline removed
peaks <- 1:length(eds.base$npks)
for (s in peaks) {
######## PKDECOMPINT ########
if (length(kerpk) > 1) {
# set number of kernels per peak manually
eds.fit[[s]] <- pkdecompint(eds.base, intnum = s,
k = kerpk[s], maxiter = fitmaxiter)
} else {
# use number of kernels determined by baselinefit()
eds.fit[[s]] <- pkdecompint(eds.base, intnum = s,
k = eds.base$npks[s], maxiter = fitmaxiter)
}
# Setup the dataframe that makes up the peak table
for (kernel in 1:eds.fit[[s]]$num.ker) {
eds.fit.parpk <- rbind(eds.fit.parpk,
data.frame(peak = factor(eds.fit[[s]]$intnr),
kernel = factor(kernel),
x = eds.fit[[s]]$parpks[kernel, "loc"],
height = eds.fit[[s]]$parpks[kernel, "height"],
area = eds.fit[[s]]$parpks[kernel, "intens"],
fwhm = eds.fit[[s]]$parpks[kernel, "FWHM"],
m = eds.fit[[s]]$parpks[kernel, "m"],
accept = eds.fit[[s]]$accept))
eds.fit.fitpk <- rbind(eds.fit.fitpk,
data.frame(peak = factor(peaks[s]),
kernel = factor(kernel),
x = eds.fit[[s]]$x,
y = eds.fit[[s]]$fitpk[kernel, ]))
}
eds.nobasl <- rbind(eds.nobasl,
data.frame(peak = factor(peaks[s]),
x = eds.fit[[s]]$x,
y = eds.fit[[s]]$y))
}
return(list(eds.base = eds.base,
eds.peaks = eds.pks,
eds.fit.parpk = eds.fit.parpk,
eds.fit.fitpk = eds.fit.fitpk,
eds.nobasl = eds.nobasl))
}