Recent changes to par_n_iter

par_n_iter modified by A. Smith

A. Smith — Thu, 08 Sep 2016 15:03:58 -0000

--- v3
+++ v4
@@ -2,4 +2,5 @@
 After each sub-spectrum is fitted, the fit is recombined to correct for peak overlap among sub-spectra, after which the fitting of sub-spectra is repeated. The number of repetitions is determined by ‘par.n_iter’. If a dynamic peak list is being used, then the iterative fitting is repeated after each peak addition/removal step. This means that the number of iterations must be specified at each step. Thus, the number of elements in ‘par.n_iter’ must be equal to [‘par.add_peaks’](/p/infos/wiki/par_add_peaks)+1. As with [‘par.IterFact’](/p/infos/wiki/par_IterFact), increasing the values in ‘par.n_iter’ will improve fitting, but improvements will be reduced as values get higher. However, because ‘par.n_iter’ changes the number of iterations that include corrections for peak overlap, increasing the value of ‘par.n_iter’ is often more beneficial than increasing the value of ‘par.IterFact’.

 For ‘par.add_peaks=5’:
-par.n_iter=\[3 3 3 3 3 6\];
+
+    par.n_iter=[3 3 3 3 3 6];

par_n_iter modified by A. Smith

A. Smith — Thu, 08 Sep 2016 14:51:34 -0000

--- v2
+++ v3
@@ -1,5 +1,5 @@
 ###par.n_iter
-After each sub-spectrum is fitted, the fit is recombined to correct for peak overlap among sub-spectra, after which the fitting of sub-spectra is repeated. The number of repetitions is determined by ‘par.n_iter’. If a dynamic peak list is being used, then the iterative fitting is repeated after each peak addition/removal step. This means that the number of iterations must be specified at each step. Thus, the number of elements in ‘par.n_iter’ must be equal to [‘par.add_peaks’](/p/infos/wiki/par_add_peaks)+1. As with ‘par.IterFact’, increasing the values in ‘par.n_iter’ will improve fitting, but improvements will be reduced as values get higher. However, because ‘par.n_iter’ changes the number of iterations that include corrections for peak overlap, increasing the value of ‘par.n_iter’ is often more beneficial than increasing the value of ‘par.IterFact’.
+After each sub-spectrum is fitted, the fit is recombined to correct for peak overlap among sub-spectra, after which the fitting of sub-spectra is repeated. The number of repetitions is determined by ‘par.n_iter’. If a dynamic peak list is being used, then the iterative fitting is repeated after each peak addition/removal step. This means that the number of iterations must be specified at each step. Thus, the number of elements in ‘par.n_iter’ must be equal to [‘par.add_peaks’](/p/infos/wiki/par_add_peaks)+1. As with [‘par.IterFact’](/p/infos/wiki/par_IterFact), increasing the values in ‘par.n_iter’ will improve fitting, but improvements will be reduced as values get higher. However, because ‘par.n_iter’ changes the number of iterations that include corrections for peak overlap, increasing the value of ‘par.n_iter’ is often more beneficial than increasing the value of ‘par.IterFact’.

 For ‘par.add_peaks=5’:
 par.n_iter=\[3 3 3 3 3 6\];

par_n_iter modified by A. Smith

A. Smith — Thu, 08 Sep 2016 14:51:08 -0000

--- v1
+++ v2
@@ -1,5 +1,5 @@
 ###par.n_iter
-After each sub-spectrum is fitted, the fit is recombined to correct for peak overlap among sub-spectra, after which the fitting of sub-spectra is repeated. The number of repetitions is determined by ‘par.n_iter’. If a dynamic peak list is being used, then the iterative fitting is repeated after each peak addition/removal step. This means that the number of iterations must be specified at each step. Thus, the number of elements in ‘par.n_iter’ must be equal to ‘par.add_peaks’+1. As with ‘par.IterFact’, increasing the values in ‘par.n_iter’ will improve fitting, but improvements will be reduced as values get higher. However, because ‘par.n_iter’ changes the number of iterations that include corrections for peak overlap, increasing the value of ‘par.n_iter’ is often more beneficial than increasing the value of ‘par.IterFact’.
+After each sub-spectrum is fitted, the fit is recombined to correct for peak overlap among sub-spectra, after which the fitting of sub-spectra is repeated. The number of repetitions is determined by ‘par.n_iter’. If a dynamic peak list is being used, then the iterative fitting is repeated after each peak addition/removal step. This means that the number of iterations must be specified at each step. Thus, the number of elements in ‘par.n_iter’ must be equal to [‘par.add_peaks’](/p/infos/wiki/par_add_peaks)+1. As with ‘par.IterFact’, increasing the values in ‘par.n_iter’ will improve fitting, but improvements will be reduced as values get higher. However, because ‘par.n_iter’ changes the number of iterations that include corrections for peak overlap, increasing the value of ‘par.n_iter’ is often more beneficial than increasing the value of ‘par.IterFact’.

 For ‘par.add_peaks=5’:
 par.n_iter=\[3 3 3 3 3 6\];

par_n_iter modified by A. Smith

A. Smith — Thu, 08 Sep 2016 10:31:32 -0000

par.n_iter

After each sub-spectrum is fitted, the fit is recombined to correct for peak overlap among sub-spectra, after which the fitting of sub-spectra is repeated. The number of repetitions is determined by ‘par.n_iter’. If a dynamic peak list is being used, then the iterative fitting is repeated after each peak addition/removal step. This means that the number of iterations must be specified at each step. Thus, the number of elements in ‘par.n_iter’ must be equal to ‘par.add_peaks’+1. As with ‘par.IterFact’, increasing the values in ‘par.n_iter’ will improve fitting, but improvements will be reduced as values get higher. However, because ‘par.n_iter’ changes the number of iterations that include corrections for peak overlap, increasing the value of ‘par.n_iter’ is often more beneficial than increasing the value of ‘par.IterFact’.

For ‘par.add_peaks=5’:
par.n_iter=[3 3 3 3 3 6];