Re: [R] R problem nls
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 > 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 0.985 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
Re: [R] R problem nls
Your formula could be simplified to y ~ 1 + a * exp(-b * x) Solve this equation for x x = ln[(y - 1)/a]/b Use this equation to find the intersection point at a given value of y. For example, when y = 1.01 x = ln(0.01/a)/b Jean Karen Vandepoel wrote on 04/09/2012 07:33:19 AM: > 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 0.985 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
[R] R problem nls
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 0.985 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 0.9
