Hi, And regarding how to extend the nls algorithm to a larger dataset it is a question of indicating in the nls() functions which data.frame to use. In you example you were using a small set of x's ad y's, so if you want to use a large set put it in a new data.frame and pass it to nls().
And if you want to repeat that calculation for many different sets of x,y (each one of them corresponding to a different wavelength) you could do that in a loop. Regards, Carlos Ortega www.qualityexcellence.es 2012/4/9 Karen Vandepoel <karen.vandep...@gmail.com> > Hi, > > I will try to explain what it is I need to do, how far I am in doing it yet > and where my problem is: > > I have a lot of x,y values I need to fit a non linear function through. > Subsequently, I need to find the intersection point of this fitted curve > with y=1.01 > > The problem is I have a lot of values so I want to be able to do it all at > once. > > I already imported my excel file of the points I have to use. > > The things I already have are following (not all of the data is visible > because otherwise the file would be too long for this email, but i just > showed part of my data so you could better understand my problem): > > > ______________ > X1242 X1242.5 X1243 X1243.5 X1244 X1244.5 > X1245 X1245.5 X1246 X1246.5 X1247 X1247.5 X1248 > X1248.5 X1249 => names of each data set ( corresponds to wavelengths, > ranges from 400 to 2500 with 0.5 steps => a lot of points!) > 18.14 0.9860316 0.9860272 0.9860203 0.9860121 0.9860044 0.9859994 0.9859971 > 0.9859976 0.9859999 0.9860035 0.9860069 0.9860103 0.9860128 0.9860151 > 0.9860178 > 15.8 0.9857134 0.9857106 0.9857063 0.9857011 0.9856958 0.9856917 0.9856887 > 0.9856874 0.9856880 0.9856893 0.9856906 0.9856919 0.9856922 0.9856916 > 0.9856912 > 13.77 0.9930109 0.9930015 0.9929921 0.9929833 0.9929765 0.9929714 0.9929674 > 0.9929637 0.9929603 0.9929569 0.9929533 0.9929501 0.9929469 0.9929440 > 0.9929416 > 9.03 0.9875374 0.9875321 0.9875242 0.9875140 0.9875024 0.9874921 0.9874840 > 0.9874793 0.9874780 0.9874802 0.9874835 0.9874869 0.9874877 0.9874857 > 0.9874801 > 6.14 0.9900554 0.9900465 0.9900376 0.9900286 0.9900204 0.9900122 0.9900032 > 0.9899924 0.9899802 0.9899669 0.9899544 0.9899445 0.9899372 0.9899333 > 0.9899317 > 4.27 1.0050327 1.0050242 1.0050175 1.0050129 1.0050107 1.0050101 1.0050094 > 1.0050070 1.0050025 1.0049959 1.0049885 1.0049812 1.0049746 1.0049691 > 1.0049647 > 2.77 0.9892697 0.9892585 0.9892454 0.9892311 0.9892164 0.9892030 0.9891906 > 0.9891796 0.9891704 0.9891624 0.9891550 0.9891480 0.9891401 0.9891320 > 0.9891235 > 1.52 0.9979284 0.9979430 0.9979548 0.9979644 0.9979739 0.9979850 0.9979984 > 0.9980137 0.9980312 0.9980498 0.9980691 0.9980897 0.9981105 0.9981323 > 0.9981542 > These are my x-values and y-values > X1249.5 X1250 X1250.5 X1251 X1251.5 X1252 > X1252.5 X1253 X1253.5 X1254 X1254.5 X1255 X1255.5 > X1256 X1256.5 > 18.14 0.9860214 0.9860261 0.9860320 0.9860377 0.9860425 0.9860456 0.9860462 > 0.9860449 0.9860433 0.9860422 0.9860417 0.9860428 0.9860444 0.9860456 > 0.9860456 > 15.8 0.9856911 0.9856918 0.9856934 0.9856958 0.9856984 0.9857014 0.9857040 > 0.9857069 0.9857099 0.9857132 0.9857153 0.9857156 0.9857132 0.9857080 > 0.9857016 > 13.77 0.9929406 0.9929405 0.9929425 0.9929445 0.9929464 0.9929476 0.9929476 > 0.9929455 0.9929431 0.9929409 0.9929377 0.9929356 0.9929331 0.9929304 > 0.9929279 > 9.03 0.9874721 0.9874641 0.9874578 0.9874542 0.9874529 0.9874541 0.9874556 > 0.9874563 0.9874565 0.9874551 0.9874527 0.9874498 0.9874461 0.9874415 > 0.9874371 > 6.14 0.9899318 0.9899319 0.9899304 0.9899263 0.9899192 0.9899095 0.9898986 > 0.9898873 0.9898777 0.9898703 0.9898641 0.9898591 0.9898546 0.9898495 > 0.9898439 > 4.27 1.0049605 1.0049564 1.0049528 1.0049495 1.0049466 1.0049435 1.0049404 > 1.0049357 1.0049298 1.0049230 1.0049155 1.0049093 1.0049049 1.0049019 > 1.0049002 > 2.77 0.9891158 0.9891100 0.9891058 0.9891036 0.9891013 0.9890986 0.9890943 > 0.9890873 0.9890789 0.9890691 0.9890583 0.9890485 0.9890395 0.9890323 > 0.9890266 > 1.52 0.9981760 0.9981970 0.9982176 0.9982372 0.9982565 0.9982753 0.9982935 > 0.9983102 0.9983259 0.9983408 0.9983533 0.9983647 0.9983749 0.9983841 > 0.9983929 > X1257 X1257.5 X1258 X1258.5 X1259 X1259.5 > X1260 X1260.5 X1261 X1261.5 X1262 X1262.5 X1263 > X1263.5 X1264 > 18.14 0.9860445 0.9860437 0.9860438 0.9860467 0.9860527 0.9860613 0.9860705 > 0.9860782 0.9860830 0.9860844 0.9860832 0.9860806 0.9860774 0.9860746 > 0.9860716 > 15.8 0.9856955 0.9856925 0.9856930 0.9856967 0.9857032 0.9857098 0.9857162 > 0.9857213 0.9857248 0.9857268 0.9857283 0.9857298 0.9857314 0.9857340 > 0.9857374 > 13.77 0.9929253 0.9929242 0.9929234 0.9929239 0.9929254 0.9929276 0.9929302 > 0.9929321 0.9929329 0.9929328 0.9929315 0.9929294 0.9929275 0.9929257 > 0.9929243 > 9.03 0.9874330 0.9874313 0.9874312 0.9874335 0.9874375 0.9874418 0.9874468 > 0.9874510 0.9874547 0.9874577 0.9874602 0.9874623 0.9874640 0.9874659 > 0.9874680 > 6.14 0.9898392 0.9898366 0.9898360 0.9898381 0.9898417 0.9898455 0.9898484 > 0.9898489 0.9898469 0.9898432 0.9898392 0.9898363 0.9898354 0.9898371 > 0.9898402 > 4.27 1.0048982 1.0048966 1.0048942 1.0048923 1.0048914 1.0048917 1.0048929 > 1.0048939 1.0048933 1.0048910 1.0048865 1.0048803 1.0048742 1.0048693 > 1.0048658 > 2.77 0.9890227 0.9890210 0.9890197 0.9890184 0.9890164 0.9890143 0.9890120 > 0.9890102 0.9890088 0.9890087 0.9890089 0.9890086 0.9890073 0.9890056 > 0.9890024 > 1.52 0.9984034 0.9984168 0.9984328 0.9984514 0.9984707 0.9984899 0.9985069 > 0.9985223 0.9985357 0.9985479 0.9985593 0.9985694 0.9985778 0.9985845 > 0.9985890 > %% I tried: > > test1<-nls(y~I(1+a*exp(1)^(-b*x)),data=model,start=list(a=1,b=1)) > Warning messages: > 1: In min(x) : no non-missing arguments to min; returning Inf > 2: In max(x) : no non-missing arguments to max; returning -Inf > > test1 > Nonlinear regression model > model: y ~ I(1 + a * exp(1)^(-b * x)) > data: model > a b > 12.58 2.66 > residual sum-of-squares: 0.0005495 > > Number of iterations to convergence: 12 > Achieved convergence tolerance: 8.038e-06 > > > > > Now I first tried it with a small data set of only 1 set of x and y values, > and found that the formula I use works so that's ok. > But now the objective is to perform the formula to the total data set and > get an overview of the a and b values *for each other data set (so for each > wavelength), not an average a and b value for everything*. > Is this possible? How do I do it? > _______________________________________________________________ > > Secondly, for the intersection point determination, I found on the internet > to use the function > >intersection(sequenceInd = NA, sequenceSig = NA, hLine = NA, plot = TRUE) > > but when I do this, the response I get is > > > intersection(sequenceInd=model,hLine=1.01,plot=TRUE) > Error: could not find function "intersection" > > The intersection line is the same for each data set. How can I find the > intersection point for each dataset? > _______________________________________________________________ > > > Thank you so much in advance, > > Karen Vandepoel > > > > -- > Karen > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > -- Saludos, Carlos Ortega www.qualityexcellence.es [[alternative HTML version deleted]] ______________________________________________ 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.
