5 messages · Jonas Stein, Sarah Goslee, David L Carlson +2 more
I want to fit discrete data that was measured on a wavegenerator. In this minimal example i generate some artificial data: testsin <- 2+ 5 * sin(1:100) #generate sin data testsin <- testsin+ rnorm(length(testsin), sd = 0.01) #add noise mydata <- list(X=1:100, Y=testsin) # generate mydata object nlmod <- nls(X ~ A+B*sin(C* Y), data=mydata, start=list(A=2, B=4, C=1), trace=TRUE) # this nls fit fails. Who can help me to fit this type of data? Kind regards,
Jonas Stein <news at jonasstein.de>
Hi Jonas, I'm afraid we need more detail.
I want to fit discrete data that was measured on a wavegenerator. In this minimal example i generate some artificial data: testsin <- 2+ 5 * sin(1:100) #generate sin data testsin <- ?testsin+ rnorm(length(testsin), sd = 0.01) #add noise mydata <- list(X=1:100, Y=testsin) # generate mydata object nlmod <- nls(X ~ A+B*sin(C* Y), data=mydata, start=list(A=2, B=4, C=1), trace=TRUE) # this nls fit fails.
Fails how? It runs when I copy and paste your code into a terminal, given:
sessionInfo()
R version 2.14.1 (2011-12-22) Platform: x86_64-redhat-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base What happens when you run it? What do you expect to happen? What's your sessionInfo()? "fails" doesn't convey enough information here. Sarah
Sarah Goslee http://www.functionaldiversity.org
Don't you want to predict testsin (aka Y) from X?
nlmod <- nls(Y ~ A+B*sin(C* X), data=mydata, start=list(A=2, B=4, C=1),
trace=TRUE) 50.42965 : 2 4 1 0.007393982 : 1.9989006 5.0014997 0.9999902 0.007377637 : 1.9989005 5.0015002 0.9999922 0.007377637 : 1.9989005 5.0015002 0.9999922
nlmod
Nonlinear regression model
model: Y ~ A + B * sin(C * X)
data: mydata
A B C
1.999 5.002 1.000
residual sum-of-squares: 0.007378
Number of iterations to convergence: 3
Achieved convergence tolerance: 1.499e-08
----------------------------------------------
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77843-4352
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Sarah Goslee
Sent: Tuesday, February 14, 2012 9:13 AM
To: Jonas Stein
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] fit data to y~A+B*sin(C*x)
Hi Jonas,
I'm afraid we need more detail.
I want to fit discrete data that was measured on a wavegenerator. In this minimal example i generate some artificial data: testsin <- 2+ 5 * sin(1:100) #generate sin data testsin <- ?testsin+ rnorm(length(testsin), sd = 0.01) #add noise mydata <- list(X=1:100, Y=testsin) # generate mydata object nlmod <- nls(X ~ A+B*sin(C* Y), data=mydata, start=list(A=2, B=4, C=1),
trace=TRUE)
# this nls fit fails.
Fails how? It runs when I copy and paste your code into a terminal, given:
sessionInfo()
R version 2.14.1 (2011-12-22) Platform: x86_64-redhat-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base What happens when you run it? What do you expect to happen? What's your sessionInfo()? "fails" doesn't convey enough information here. Sarah
Sarah Goslee http://www.functionaldiversity.org ______________________________________________ R-help at 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.
I want to fit discrete data that was measured on a wavegenerator. In this minimal example i generate some artificial data: testsin <- 2+ 5 * sin(1:100) #generate sin data testsin <- testsin+ rnorm(length(testsin), sd = 0.01) #add noise mydata <- list(X=1:100, Y=testsin) # generate mydata object nlmod <- nls(X ~ A+B*sin(C* Y), data=mydata, start=list(A=2, B=4, C=1), trace=TRUE) # this nls fit fails.
What do you mean by "fail"
nlmod
Nonlinear regression model
model: X ~ A + B * sin(C * Y)
data: mydata
A B C
50.7553 0.6308 0.8007
residual sum-of-squares: 83308
Number of iterations to convergence: 24
Achieved convergence tolerance: 7.186e-06
Results don't seem to look ok.
But I think you made a small mistake in the formula.
The argument to sin in testsin is 1:100 but that's not what you are giving nls.
Try this
nlmod <- nls(Y ~ A+B*sin(C* X), data=mydata, start=list(A=2, B=4, C=1), trace=TRUE)
50.30593 : 2 4 1 0.01014092 : 2.0003732 5.0002681 0.9999979 0.01014016 : 2.0003732 5.0002681 0.9999983
nlmod
Nonlinear regression model model: Y ~ A + B * sin(C * X) data: mydata A B C 2 5 1 residual sum-of-squares: 0.01014 Number of iterations to convergence: 2 Achieved convergence tolerance: 1.201e-07 Looks better? Berend
Note that, given C, A and B can be obtained by simple linear regression of y on sin(Cx). Hence you could avoid nls altogether by a simple search of the minimal ls solution(possibly robust) over a grid of C values. Or do this to find good starting values for nls. Bert Sent from my iPhone -- please excuse typos.
nlmod