Re: [R] multiple peak fit
Thank you Jean
Basically I know that it is possible, however it is quite sensitive to starting
values and those 2 peaks do not provide perfect fit. There is probably third
peak between those 2 (based on other results).
But if I put third peak to nls I get an error
> fit <- nls(sig ~ d +
+ a1*exp(-0.5*((time-c1)/b1)^2) +
+ a2*exp(-0.5*((time-c2)/b2)^2)+
+ a3*exp(-0.5*((time-c3)/b3)^2),
+ start=list(a1=12.3, b1=18, c1=315, a2=.5, b2=80, c2=390, a3=3, b3=90, c3=480,
d=1.5), data=temp)
Error in nls(sig ~ d + a1 * exp(-0.5 * ((time - c1)/b1)^2) + a2 * exp(-0.5 * :
singular gradient
There has to be another option which seems ALS function is based on but the
source is rather complicated for my math knowledge.
Anyway, thank for your code.
Petr
From: Adams, Jean [mailto:jvad...@usgs.gov]
Sent: Thursday, March 21, 2013 10:58 PM
To: PIKAL Petr
Cc: r-help@r-project.org
Subject: Re: [R] multiple peak fit
Petr,
You could use nonlinear regression to fit two Guassian peaks. For example,
fit <- nls(sig ~ d + a1*exp(-0.5*((time-c1)/b1)^2) +
a2*exp(-0.5*((time-c2)/b2)^2),
start=list(a1=15, b1=20, c1=315, a2=4, b2=50, c2=465, d=1.5),
data=temp)
plot(temp, type="l")
lines(temp$time, predict(fit), lty=2, col="red")
Jean
On Thu, Mar 21, 2013 at 8:17 AM, PIKAL Petr
mailto:petr.pi...@precheza.cz>> wrote:
Hi
I went through some extensive search to find suitable method (package,
function) to fit multiple peaks. The best I found is ALS package but it
requires rather complicated input structure probably resulting from GC-MS
experimental data and seems to be an overkill to my problem.
I have basically simple two column data frame with columns time and sig
dput(temp)
structure(list(time = c(33, 34, 37.3, 44.7, 56, 70, 85,
99.7, 114, 127.3, 140, 151.7, 163, 174, 185, 196,
206.7, 217.3, 228, 238.7, 249.3, 260, 271, 282,
292.7, 303.7, 314.3, 325.3, 336, 346.7, 357.3,
368, 379, 389.7, 400.3, 411, 421.7, 432.3, 443,
454, 465, 476, 487, 497.7, 508.3, 519, 530, 541, 551.7,
562.3, 573, 584, 595, 606, 616.7, 627.3, 638, 649,
659.7, 670.3, 681, 692, 703, 713.7, 724.3, 735,
746, 757, 768, 779, 789), sig = c(1.558, 1.5549, 1.5619, 1.5614,
1.5618, 1.6044, 1.6161, 1.6287, 1.6432, 1.6925, 1.7273, 1.6932,
1.669, 1.6863, 1.6962, 1.7186, 1.7513, 1.8325, 1.9181, 2.01,
2.1276, 2.2821, 2.5596, 2.9844, 4.1272, 13.421, 15.422, 14.119,
11.491, 8.8799, 6.7774, 5.6223, 4.8775, 4.3628, 4.0517, 3.9146,
3.8704, 3.9162, 4.0372, 4.0948, 4.2054, 4.1221, 3.9145, 3.5724,
3.2108, 2.8311, 2.4605, 2.1985, 1.9685, 1.8158, 1.7487, 1.692,
1.6565, 1.6374, 1.609, 1.5927, 1.5401, 1.5366, 1.5614, 1.5314,
1.4989, 1.5053, 1.4953, 1.4824, 1.4743, 1.4468, 1.4698, 1.4671,
1.4675, 1.4704, 1.4966)), .Names = c("time", "sig"), row.names = c(NA,
-71L), class = "data.frame")
When it is plotted there are clearly 2 peaks visible.
plot(temp, type="l")
Does anybody know how to decompose those data to two (or more) gaussian (or
other) peaks?
I can only think about nlme but before I start from scratch I try to ask others.
Best regards
Petr
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Re: [R] multiple peak fit
Petr,
You could use nonlinear regression to fit two Guassian peaks. For example,
fit <- nls(sig ~ d + a1*exp(-0.5*((time-c1)/b1)^2) +
a2*exp(-0.5*((time-c2)/b2)^2),
start=list(a1=15, b1=20, c1=315, a2=4, b2=50, c2=465, d=1.5), data=temp)
plot(temp, type="l")
lines(temp$time, predict(fit), lty=2, col="red")
Jean
On Thu, Mar 21, 2013 at 8:17 AM, PIKAL Petr wrote:
> Hi
>
> I went through some extensive search to find suitable method (package,
> function) to fit multiple peaks. The best I found is ALS package but it
> requires rather complicated input structure probably resulting from GC-MS
> experimental data and seems to be an overkill to my problem.
>
> I have basically simple two column data frame with columns time and sig
>
> dput(temp)
> structure(list(time = c(33, 34, 37.3, 44.7, 56, 70, 85,
> 99.7, 114, 127.3, 140, 151.7, 163, 174, 185, 196,
> 206.7, 217.3, 228, 238.7, 249.3, 260, 271, 282,
> 292.7, 303.7, 314.3, 325.3, 336, 346.7, 357.3,
> 368, 379, 389.7, 400.3, 411, 421.7, 432.3, 443,
> 454, 465, 476, 487, 497.7, 508.3, 519, 530, 541, 551.7,
> 562.3, 573, 584, 595, 606, 616.7, 627.3, 638, 649,
> 659.7, 670.3, 681, 692, 703, 713.7, 724.3, 735,
> 746, 757, 768, 779, 789), sig = c(1.558, 1.5549, 1.5619, 1.5614,
> 1.5618, 1.6044, 1.6161, 1.6287, 1.6432, 1.6925, 1.7273, 1.6932,
> 1.669, 1.6863, 1.6962, 1.7186, 1.7513, 1.8325, 1.9181, 2.01,
> 2.1276, 2.2821, 2.5596, 2.9844, 4.1272, 13.421, 15.422, 14.119,
> 11.491, 8.8799, 6.7774, 5.6223, 4.8775, 4.3628, 4.0517, 3.9146,
> 3.8704, 3.9162, 4.0372, 4.0948, 4.2054, 4.1221, 3.9145, 3.5724,
> 3.2108, 2.8311, 2.4605, 2.1985, 1.9685, 1.8158, 1.7487, 1.692,
> 1.6565, 1.6374, 1.609, 1.5927, 1.5401, 1.5366, 1.5614, 1.5314,
> 1.4989, 1.5053, 1.4953, 1.4824, 1.4743, 1.4468, 1.4698, 1.4671,
> 1.4675, 1.4704, 1.4966)), .Names = c("time", "sig"), row.names = c(NA,
> -71L), class = "data.frame")
>
> When it is plotted there are clearly 2 peaks visible.
>
> plot(temp, type="l")
>
> Does anybody know how to decompose those data to two (or more) gaussian
> (or other) peaks?
>
> I can only think about nlme but before I start from scratch I try to ask
> others.
>
> Best regards
> Petr
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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and provide commented, minimal, self-contained, reproducible code.
[R] multiple peak fit
Hi
I went through some extensive search to find suitable method (package,
function) to fit multiple peaks. The best I found is ALS package but it
requires rather complicated input structure probably resulting from GC-MS
experimental data and seems to be an overkill to my problem.
I have basically simple two column data frame with columns time and sig
dput(temp)
structure(list(time = c(33, 34, 37.3, 44.7, 56, 70, 85,
99.7, 114, 127.3, 140, 151.7, 163, 174, 185, 196,
206.7, 217.3, 228, 238.7, 249.3, 260, 271, 282,
292.7, 303.7, 314.3, 325.3, 336, 346.7, 357.3,
368, 379, 389.7, 400.3, 411, 421.7, 432.3, 443,
454, 465, 476, 487, 497.7, 508.3, 519, 530, 541, 551.7,
562.3, 573, 584, 595, 606, 616.7, 627.3, 638, 649,
659.7, 670.3, 681, 692, 703, 713.7, 724.3, 735,
746, 757, 768, 779, 789), sig = c(1.558, 1.5549, 1.5619, 1.5614,
1.5618, 1.6044, 1.6161, 1.6287, 1.6432, 1.6925, 1.7273, 1.6932,
1.669, 1.6863, 1.6962, 1.7186, 1.7513, 1.8325, 1.9181, 2.01,
2.1276, 2.2821, 2.5596, 2.9844, 4.1272, 13.421, 15.422, 14.119,
11.491, 8.8799, 6.7774, 5.6223, 4.8775, 4.3628, 4.0517, 3.9146,
3.8704, 3.9162, 4.0372, 4.0948, 4.2054, 4.1221, 3.9145, 3.5724,
3.2108, 2.8311, 2.4605, 2.1985, 1.9685, 1.8158, 1.7487, 1.692,
1.6565, 1.6374, 1.609, 1.5927, 1.5401, 1.5366, 1.5614, 1.5314,
1.4989, 1.5053, 1.4953, 1.4824, 1.4743, 1.4468, 1.4698, 1.4671,
1.4675, 1.4704, 1.4966)), .Names = c("time", "sig"), row.names = c(NA,
-71L), class = "data.frame")
When it is plotted there are clearly 2 peaks visible.
plot(temp, type="l")
Does anybody know how to decompose those data to two (or more) gaussian (or
other) peaks?
I can only think about nlme but before I start from scratch I try to ask others.
Best regards
Petr
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
