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