problem with nlsLM.....

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problem with nlsLM.....

akshay kulkarni
dear members,
                             Yesterday, Duncan identified a silly mistake in an nls call...in the list of starting values, one of the names of the variable was getting repeated. But I am experiencing the same problem again, with a different formula:

> formulaDH3
HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))

here HM1 is the response variable, and HM2 and HM3 are predictors.....

> nonlin_modDH3 <- nls(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
Error in nlsModel(formula, mf, start, wts) :
  singular gradient matrix at initial parameter estimates
> nonlin_modDH3 <- nlsLM(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
Error in nlsModel(formula, mf, start, wts) :
  singular gradient matrix at initial parameter estimates

I am using nlsLM function from the minpack.lm package which says that it will converge when nls fails with singular gradient matrix error...


Is there again a silly mistake(pardon me again if there is one!), or is the problem serious? If it is serious, any pointers towards a solution?

very many thanks for your time and effort....
yours sincerely,
AKSHAY M KULKARNI

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Fw: problem with nlsLM.....

akshay kulkarni
dear members,
                             I think Duncan's  solution is not working...I've checked the function again:

> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) + (A*sin(w*HM3 + c) + C))
> nonlin_modDH5 <- nls(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
Error in nlsModel(formula, mf, start, wts) :
  singular gradient matrix at initial parameter estimates
> nonlin_modDH5 <- nlsLM(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
Error in nlsModel(formula, mf, start, wts) :
  singular gradient matrix at initial parameter estimates


I've checked the Internet  for a method of getting the starting values, but they are not comprehensive....any resources for how to find the starting values?

> dput(HM1)
c(1.01484280343579, 0.977004104656811, 1.02448653468064, 1.01562102015533,
0.968359321714289, 0.99105072887847, 0.99384582083167, 1.00579414727333,
0.967593120445675, 1.04694148058855, 1.03832347454026, 0.971040156128456,
0.976967848281943, 0.990386686964446, 1.02257020722784, 0.994529161072552,
1.05361176882747, 0.988021514764882, 0.998909692369571, 1.04933819895509,
0.988354009297047, 0.878584020586706, 1.01438895222572, 1.01522203155842,
0.999979601913003, 0.975862934766315, 1.03948445565434, 1.02493339899053,
0.972267168470219, 0.971001051421653, 1.03066167719129, 0.967898970631469,
0.976606662010519, 0.990061873979734, 1.03398666689317, 1.05491455733517,
0.995997711923663, 0.991213393857838, 0.9903216916016, 1.06850794640807,
1.01345738121581, 0.877847194198055, 0.992124544661245, 1.00927272338804,
0.988196514422814, 0.987504905313256, 0.990274516132497, 1.01484624802751,
0.9800176904083, 0.969321725342799, 1.07407427451262, 1.02023130729371,
0.975827723120998, 0.988260886231393, 1.05116598585815, 0.977996171738906,
0.985118207891832, 0.98243346941999, 0.991380612684958, 1.00960741365585,
1.03062446605116, 1.04176358759364, 1.02494798972349, 1.04645592406625,
0.999998705820515, 0.972161829098397, 0.991313521356105, 1.0211561076261,
0.971257832217016, 1.00935026461508, 0.997140402835885, 1.00719609467891,
0.97483865743955, 0.987491380127905, 1.0643888147255, 1.00804564512905,
0.989366232219398, 0.942042386899482, 1.0140328534339, 0.98812919092058,
1.04584882138055, 0.970586857498767, 1.0098232311784, 1.02237732533005,
0.99306513943649, 0.963808401790738, 1.03577231048959, 1.00540101137939,
0.970458918298484, 0.973460294926879, 0.992676651187501, 1.04079931972326,
0.988928168736541, 0.986376934855979, 1.04117941173535, 1.01252015337269,
0.920578394310677, 0.98933093765412, 0.98116698970429, 0.995498900493761,
0.96463760451942, 1.1059686605887, 1.03366353582116, 0.981256131441973,
1.0152599452654, 0.995362080418312, 0.997276311537458, 0.943438355295013,
1.04025117202718, 1.03102737097366, 1.02096902615856, 1.02506560388494,
0.959364103040633, 0.987217067188943, 1.00454456275356, 1.03517749036747,
1.0148342900634, 0.974518005568706, 1.02515903830848, 1.00435361604303,
0.973400954462685, 0.981702992961201, 1.0155969247203, 1.01064154947471,
0.957131570492054, 0.88504625471737, 1.01849907662046, 1.01503806358198,
0.987004149653273, 0.974485857983481, 0.987572979379099, 1.0037529762248,
0.976026658183349, 0.983176178747953, 1.03812943411666, 1.01828203593425,
0.984139742592733, 1.00377243020886, 1.02118652563454, 1.01115776483144,
0.98387406692053, 0.985919715423209, 0.989550590454508, 1.01845373071313,
1.00394660346757, 0.949076679285431, 1.06094051230739, 0.981940494629297,
0.982812578115724, 0.929673163266673, 1.05359335975355, 1.03498999963467,
0.989218608625026, 0.994412680504384, 1.03162650392014, 1.04359975620911,
0.96938669600183, 0.987150178669349, 0.990503131271205, 1.01120231626779,
0.977334529946535, 0.966013274319284, 1.04966722359666, 1.04745672849702,
0.960799643211444, 0.866626315091455, 1.01316731305781, 1.02919067036911,
0.988567285725018, 0.978702298855537, 1.0186604054056, 1.01398864226215,
0.985492431198792, 1.03982326960775, 1.02287049736119, 1.02357106122821,
0.977519287403578, 0.993818572820706, 1.01139405704906, 1.02683235508704,
0.943370973180458, 0.970153991236375, 0.970746163945049, 1.00996886938921,
0.970599365922215, 1.03230385881209, 1.02072718512322, 1.0161024503161,
0.987718207880278, 0.987512809150475, 1.04108232940019, 1.0153758143276,
1.03916475100072, 0.959776942350879, 1.01739288375256, 1.05438479662213,
0.970479916939806, 0.979826128477137, 0.982800613999074, 0.977050857426291,
0.977474025608337, 0.972905352371901, 1.02191626900472, 1.02737043327345,
0.987565251408109, 0.93131925605196, 0.991469304659888, 0.949708678221412,
0.948662234856045, 0.938158485016718, 1.02342896552764, 1.01869899424866,
1.01244484998263, 0.990637832491416, 1.04591108144934, 0.951244278685872,
0.977265525807748, 0.942206977147219, 1.09440128638859, 1.02935716108991,
0.978428723706514, 1.08589930790608, 1.0015813231405, 1.07512822987239,
0.976356469419301, 0.972775004620642, 1.00954673593815, 0.992551447194369,
0.985819189451289, 0.975310750212573, 1.05999783258221, 1.07815688799274,
0.959648862885262, 1.03418767541976, 1.07207529899183, 1.04949264142055,
0.966872387358102, 0.963564695272614, 1.01310836328613, 0.996622968927382,
1.00202486063801, 0.948215304098709, 1.02845448262427, 0.987124642586768,
0.999269328077687, 0.964674178364545, 0.991913103743918, 0.970556592778505,
0.979495816235886, 0.949160643807912, 0.999611005677085, 1.01697727168167,
0.966362681248743, 0.938703012004999, 1.01394761650376, 1.011369485146,
0.975929406359934, 0.97111480362557, 1.0581474202687, 1.03965938660391,
0.990447147628825, 0.967286377976687, 0.968162001740811, 1.00573452177125,
0.974628505381728, 0.983183640545729, 1.08185959707149, 0.976533333333333,
0.932582771965921, 1.02205682784386, 1.02162002635618, 1.00634335266967,
1.03045302554561, 1.05015537832235, 1.0238800474742, 1.0151188247761,
0.951176244898346, 0.937999477475998, 0.986664690881963, 1.00889938014614,
0.972546365957459, 0.937560562249233, 0.953227957137494, 0.964284336810156,
0.977909815090698, 0.988068102557503, 1.04429493287078, 1.01803069781624,
0.957632272450841, 0.971991500790857, 0.98209395086599, 1.02815448263624,
0.947227198465786, 0.941538843776026, 1.05745885548346, 1.01671992454154,
0.983992269183969, 0.951511089781983, 1.0110983382201, 1.00912039359684,
1.14741321154181, 0.985344569298617, 0.986725720078838, 1.03117619992574,
1.21391930217405, 0.997217690041687, 1.07387278335981, 1.02108776576674,
1.10313006343474, 1.10905015571117, 1.03999508931616, 1.03976033898847,
1.02524837903366, 0.984818521675578, 1.04179889608739, 0.975154632188378,
0.969607959579886, 1.02781726918172, 0.999963290601752, 1.01515310515696,
1.00520167771997, 0.984550628692601, 1.01902810624565, 1.00685219196689,
0.982173452231062, 0.999629285915004, 0.970558368481076, 0.995716235126211,
1.01314341676203, 1.02075675497478, 1.01128668171558, 1.05844449406008,
1.02510935720045, 0.983720171349822, 0.844396082698583, 1.05810846093782,
0.99048977314373, 0.971262209252963, 0.968530578900656, 1.04638759101296,
1.1799017507928, 0.898448146333193, 1.01160891745375, 1.01132611427775,
0.973840647644128, 1.04683923857471, 0.990344793003927, 1.0068683076688,
0.992798241837764, 0.99368375299494, 1.01200003722717, 1.0294016781563,
1.01547984207734, 1.04807175686468, 0.981737356425208, 1.06169292627851,
1.00518291863648, 0.976808854545251, 0.999321592083517, 0.97527078961014,
0.904376968942001, 0.987807226191329, 1.0350293488462, 1.02802944623052,
0.963692701708712, 0.998027399233401, 1.01762747994825, 1.0361812777154,
0.988298193013531, 1.04174794125517, 0.970839140069407, 1.0011387710754,
0.961691863428911, 1.02272120871699, 1.00519180537531, 0.999331326197155,
0.988055521308447, 1.00078630058504, 1.03915754537212, 0.971719181925134,
0.947846525645955, 0.955178087494908, 1.0019107391621, 1.01160524115586,
0.984958748948305, 0.967579294820259, 1.00552471207069, 1.04518978636097,
1.01810784117456, 0.993552343701494, 1.03535983577691, 0.982973964981781,
0.976175417305875, 0.971173421374951, 1.02622192154044, 1.06169292627851,
0.980316131595686, 0.98802873807095, 1.02522486330609, 1.01920704714191,
0.987730950969463, 0.972320506142784, 1.02950031603837, 1.01481027236895,
1.05066348592531, 0.941412086703545, 1.00426249434292, 0.997599618540051,
0.9727707483253, 0.987822018689403, 1.00496006171741, 1.00365488022554,
0.982021737672154, 0.946014836456916, 0.977232083157459, 1.04246004468132,
0.981228463109654, 0.971195860055527, 0.957569129533581, 0.987312165664453,
0.955583610052133, 0.991173211015633, 0.988434021882053, 0.94971029297998,
1.02260060019826, 0.986729432665294, 1.00044978044626, 1.00283798105021,
0.97876379280085, 0.948623031823408, 1.02317511219612, 1.02274213461243,
0.952138762776323, 0.974014522860041, 1.02874293788325, 1.02108205408977,
0.950582622331241, 0.986098109021331, 1.00098916530354, 1.05435029523389,
0.999921440746985, 0.964778188342829, 1.02087753056688, 1.0295810738595,
0.981181990342875, 0.94981916198993, 1.00828646284387, 1.01127872412662,
0.976192285243012, 0.95032633282931, 1.02884118341175, 1.01603776783957,
0.952947066257398, 0.988924105443658, 1.02727453593468, 1.02944328284254,
0.970561666318556, 0.982859453938812, 0.995886713501412, 1.04330648532137,
0.991844865771659, 0.99159250467833, 1.0332159700053, 0.993864541664301,
0.952716253755034, 0.960889912140351, 0.945669021854222, 0.985578531230794,
0.975053113912155, 0.999552576905685, 1.02526821151208, 1.00450298718514,
0.948193972002236, 0.99358065264941, 1.04855364680554, 0.982181523974446,
0.957570494265717, 0.989249667650167, 1.01928030167592, 0.977392376116208,
0.98989833978368, 0.950312758756191, 1.01179069272545, 1.03630963397181,
0.997439452068598, 0.981793158114732, 0.98560918506727, 1.0497573745934,
1.06729226949522, 0.987962794653476, 1.05655507163022, 1.01896087310511,
0.985627836611195, 1.03148215915346, 1.07268555224398, 1.00116324087534,
1.0353274520202, 0.965843323254256, 0.981731445105197, 1.01927876056569,
0.999117162412819, 0.984262994383997, 1.0058994935294, 1.03100765642408,
0.970000664632288, 0.974667000854514, 1.02683721041632, 1.00224180239507,
0.976488446884133, 0.989129579180466, 1.01111944121172, 1.01081759149299,
0.990822520380867, 0.951849309560414, 1.0533258785109, 1.02605855087945,
0.993203883495146, 1.01231349444546, 1.05040134097269, 0.988195090931879,
0.998324259112233, 0.955268105597824, 1.01179535756103, 1.00923640076821,
1.01018973127051, 0.97727388908476, 1.0163531272719, 1.03445181115965,
0.922709820364792, 1.05034425545701, 0.971929096725562, 1.01600521659268,
0.989307124797035, 0.966327502991904, 1.0148486097639, 1.00804453317389,
0.986688556832374, 1.01100519377607, 1.07022911567444, 1.01751745478207,
1.00395256916996, 1.02666291685486, 1.04306886282915, 0.976284796774505,
0.873912939480404, 0.985956634379203, 0.966005821428092, 1.01911378235808,
0.989971040708023, 0.948518684869746, 0.964578663605958, 1.00130118816861,
1.14681217078448, 0.989198271209915, 0.975831372743337, 0.87134009583841,
0.999320371256267, 1.04366317607907, 1.0941742251269, 1.04912747347822,
0.873169695550468, 0.9946703855144, 1.02687904508206, 1.01557590029223,
0.979039928979583, 0.978199700239161, 0.988038704848986, 0.944583404359117,
0.97831517569251, 0.9881631354812, 0.990553852862955, 1.05139702349736,
0.987315661692795, 0.973936995140175, 1.06038641236987, 1.0651525599012,
0.997891074790299, 0.979657304627723, 0.976638433723023, 0.981157058049022,
0.974773098042751, 1.04814547433216, 1.02327857487465, 1.01725074945885,
0.978364366421386, 1.02726007295919, 1.02997589351422, 1.0208112362723,
0.965389721633011, 1.01152741033802, 1.00352090319123, 0.981151102088714,
0.986010324656192, 0.97990659165448, 0.990503768576242, 0.985301104774446,
0.976384165205197, 0.983887706958686, 0.992885571845564, 0.985757622165226,
0.946874017209482, 0.990962963812142, 1.04319324396693, 1.07354098057006,
0.984597785175012, 0.97845443949194, 0.963960215777945, 1.06584156194012,
1.15623372835712, 0.94928101059165, 1.05512096334013, 1.00899261027951,
1.00760894032175, 0.989021593753401, 1.01442722705518, 1.04495346859144,
1.00095646335122, 0.958907195434402, 1.02045280866442, 1.00659622664478,
0.980590749755976, 1.01197688939839, 0.998468538022796, 1.040625279257,
0.934164344256759, 0.992160161968429, 0.998594942355232, 1.0532781805963,
0.974144103277039, 0.946253316413415, 1.00623102828924, 0.988787044999963,
0.987541467770869, 0.999140303075891, 1.05429436081972, 1.01617267860198,
1.17044068684611, 1.05663112916056, 1.01681282884482, 1.02872692218644,
1.00738468152863, 1.0222081131894, 1.01817103171172, 1.01222250929532,
0.97386289237006, 0.987701353725335, 1.00418624697519, 1.0183108729199,
0.956852868616883, 1.05440980534023, 1.01562402108019, 1.00083274000183,
0.945641654423597, 0.966367379950251, 1.02020174816679, 1.01151664724635,
0.940072052642951, 0.991027873457432, 0.984568985852697, 1.02373049431651,
0.985887377562884, 1.05498951399906, 0.98672427011774, 1.02570399134917,
1.06987898201568, 1.02648218241568, 0.971175525661877, 1.03211886190237,
0.992647697074835, 0.986388969656675, 1.02097305537141, 1.04518978636097,
0.987300416455667, 0.985182286987375, 1.02373433952904, 1.01376295257605,
0.992398195656577, 0.983765510339449, 1.06154013507033, 1.01312319643703
)


> dput(HM3)
c(0.196622289085315, -0.0436031046458981, 0.000820037203846464,
0.392758326108918, 0.0445839547736504, 0.223969403187679, -0.070152338130932,
0.197471312884591, 0.0498910549108242, -0.620322188497616, 0.488037411730214,
0.0461937792271113, 0.0051066586622384, 0.413877974790452, -0.154637441642314,
0.229802002983503, 0.509904610124243, 0.126452664534026, 0.583037470359474,
0.470621010682895, -0.00578078533090776, -0.457074922218691,
-0.0506356375273661, -0.767302691971105, -0.000822439375515026,
-0.170080770417037, -0.0638533091192526, 0.218492017287635, -0.088210288825341,
-0.389219867379597, 1.23355646106213, -1.78773586807154, 0.0117401974737664,
-0.154882548871051, -2.11547741687211, -0.0638533091192526, -8.18190789706224,
0.282699360997887, 0.0383693451361519, -0.0638533091192526, 0.832793868600698,
0.894379977535782, -0.302329357989944, 0.573489523345283, -0.223540700260161,
0.229802002983503, 0.0239731624923989, 0.556201388601996, -0.112552578411774,
-0.0438764575701239, 0.470621010682895, -4.92500180132548, 0.00309129784132211,
0.172407876390051, 0.229802002983503, 0.061799194890529, -0.000864150216291216,
-0.0683834224257835, -1.42874041532869, 0.654009227783557, -0.620322188497616,
0.272034153890376, -0.00558557185457871, 0.229802002983503, -1.53118563423569e-05,
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0, 0.286160612098488, 0.101207913446722, 0.374419900839882, -0.106217410937095,
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3.84024898682852, -0.0638533091192526, 0.389584248100691, 0.0449003257302091,
1.02228506672615, 0.229802002983503, -0.0638533091192526, -0.0829134652872339,
3.50596545804498, -0.389219867379597, 0.9170368554337, -0.0638533091192526,
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1.63476338054133, 0, -0.358533993792374, -0.180900357181005,
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0.999426501553857, -0.0638533091192526, -0.894190901951266, 0.0608976332239153,
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0.568835467760944, 0.196290116211879, 0.00657090150018764, 1.15108667563071,
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0.00453833599472268, 0.334589717726727, 0.470621010682895, -0.390600326717378,
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1.1517268134498, -0.0027549233829829, -5.20036700047062, -0.389219867379597,
1.38009140442044, 0.188112123387496, -0.389219867379597, -0.0715310034898503,
0.229802002983503, -1.42113754281828, 0.0671280483594289, -0.866540256758301,
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0.470621010682895, 0.470621010682895, 0.0211915910476394, -0.00153721649859708,
-0.0638533091192526, -0.102776844490052)

> dput(HM2)
c(0.016497438441392, -0.0121005433105642, 0.0340103138875006,
0.0393128837416979, 0.144718671661076, 0.0775923152157973, 0.000894372752462123,
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0.0270242177828994, 0.0650551686301645, 0.01402255124967)


very many thanks for your time and effort...
yours sincerely

AKSHAY M KULKARNI


________________________________________
From: R-help <[hidden email]> on behalf of akshay kulkarni <[hidden email]>
Sent: Wednesday, March 20, 2019 1:32 PM
To: R help Mailing  list
Subject: [R] problem with nlsLM.....

dear members,
                             Yesterday, Duncan identified a silly mistake in an nls call...in the list of starting values, one of the names of the variable was getting repeated. But I am experiencing the same problem again, with a different formula:

> formulaDH3
HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))

here HM1 is the response variable, and HM2 and HM3 are predictors.....

> nonlin_modDH3 <- nls(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
Error in nlsModel(formula, mf, start, wts) :
  singular gradient matrix at initial parameter estimates
> nonlin_modDH3 <- nlsLM(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
Error in nlsModel(formula, mf, start, wts) :
  singular gradient matrix at initial parameter estimates

I am using nlsLM function from the minpack.lm package which says that it will converge when nls fails with singular gradient matrix error...


Is there again a silly mistake(pardon me again if there is one!), or is the problem serious? If it is serious, any pointers towards a solution?

very many thanks for your time and effort....
yours sincerely,
AKSHAY M KULKARNI

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Re: problem with nlsLM.....

Ivan Krylov
In reply to this post by akshay kulkarni
On Wed, 20 Mar 2019 08:02:45 +0000
akshay kulkarni <[hidden email]> wrote:

> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) +
> (A*sin(w*HM3 + c) + C))

The problem with this formula is simple: the partial derivative with
respect to `a` is the same as the partial derivative with respect to
`C`. This makes the regression problem have an infinite number of
solutions, all of them satisfying equation \lambda_1 * a + \lambda_2 *
C + \lambda_3 = 0 for some values of \lambda_i. Gradient-based
optimizers (which both nls and nlsLM are) don't like problems with
non-unique solutions, especially when the model function has same
partial derivative with respect to different variables, making them
indistinguishable.

Solution: remove one of the variables.

> > formulaDH3  
> HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))

The problem with this formula is similar, albeit slightly different.

Suppose that (a, b, c) is a solution. Then (\lambda * a, \lambda * b,
c / \lambda) is also a solution for any real \lambda. Once again,
removing `c` should get rid of ambiguity.

> I've checked the Internet  for a method of getting the starting
> values, but they are not comprehensive....any resources for how to
> find the starting values?

Starting values depend on the particular function you are trying to
fit. The usual approach seems to be in transforming the formula and
getting rid of parts you can safely assume to be small until it looks
like linear regression, or applying domain specific knowledge (e.g.
when trying to it a peak function, look for the biggest local maximum
in the dataset).

If you cannot do that, there also are global optimization algorithms
(see `nloptr`), though they still perform better on some problems
and worse on others. It certainly helps to have upper and lower
bounds on all parameter values. I've heard about a scientific group
creating a pool of many initial Levenberg-Marquardt parameter estimates,
then improving them using a genetic algorithm. The whole thing
"converged overnight" on a powerful desktop computer.

--
Best regards,
Ivan

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Re: problem with nlsLM.....

akshay kulkarni
dear Ivan,
                   THanks for the reply......
But doesn't removing some of the parameters reduce the precision of the relationship between the response variable and the predictors(inefficient estimates of the coefficients)?

very many thanks for your time and effort....
yours sincerely,
AKSHAY M KULKARNI
________________________________
From: Ivan Krylov <[hidden email]>
Sent: Wednesday, March 20, 2019 3:06 PM
To: akshay kulkarni
Cc: R help Mailing list
Subject: Re: [R] problem with nlsLM.....

On Wed, 20 Mar 2019 08:02:45 +0000
akshay kulkarni <[hidden email]> wrote:

> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) +
> (A*sin(w*HM3 + c) + C))

The problem with this formula is simple: the partial derivative with
respect to `a` is the same as the partial derivative with respect to
`C`. This makes the regression problem have an infinite number of
solutions, all of them satisfying equation \lambda_1 * a + \lambda_2 *
C + \lambda_3 = 0 for some values of \lambda_i. Gradient-based
optimizers (which both nls and nlsLM are) don't like problems with
non-unique solutions, especially when the model function has same
partial derivative with respect to different variables, making them
indistinguishable.

Solution: remove one of the variables.

> > formulaDH3
> HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))

The problem with this formula is similar, albeit slightly different.

Suppose that (a, b, c) is a solution. Then (\lambda * a, \lambda * b,
c / \lambda) is also a solution for any real \lambda. Once again,
removing `c` should get rid of ambiguity.

> I've checked the Internet  for a method of getting the starting
> values, but they are not comprehensive....any resources for how to
> find the starting values?

Starting values depend on the particular function you are trying to
fit. The usual approach seems to be in transforming the formula and
getting rid of parts you can safely assume to be small until it looks
like linear regression, or applying domain specific knowledge (e.g.
when trying to it a peak function, look for the biggest local maximum
in the dataset).

If you cannot do that, there also are global optimization algorithms
(see `nloptr`), though they still perform better on some problems
and worse on others. It certainly helps to have upper and lower
bounds on all parameter values. I've heard about a scientific group
creating a pool of many initial Levenberg-Marquardt parameter estimates,
then improving them using a genetic algorithm. The whole thing
"converged overnight" on a powerful desktop computer.

--
Best regards,
Ivan

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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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Re: problem with nlsLM.....

Ivan Krylov
On Wed, 20 Mar 2019 09:43:11 +0000
akshay kulkarni <[hidden email]> wrote:

> But doesn't removing some of the parameters reduce the precision of
> the relationship between the response variable and the
> predictors(inefficient estimates of the coefficients)?

No, it doesn't, since there is already more variables in the formula
than it has relationships between response and predictors.

Let me offer you an example. Suppose you have a function y(x) = a*b*x +
c. Let's try to simulate some data and then fit it:

# choose according to your taste
a <- ...
b <- ...
c <- ...

# simulate model data
abc <- data.frame(x = runif(100))
abc$y <- a*b*abc$x + c
# add some normally distributed noise
abc$y <- abc$y + rnorm(100, 0, 0.01)

Now try to fit formula y ~ a*b*x + c using data in data frame abc. Do
you get any results? Do they match the values you have originally
set?[*]

Then try a formula with the ambiguity removed: y ~ d*x + c. Do you get a
result? Does the obtained d match a*b you had originally set?

Note that for the d you obtained you can get an infinite amount of
(a,b) tuples equally satisfying the equation d = a*b and the original
regression problem, unless you constrain a or b.

--
Best regards,
Ivan

[*] Using R, I couldn't, but the nonlinear solver in gnuplot is
sometimes able to give *a* result for such a degenerate problem when
data is sufficiently noisy. Of course, such a result usually doesn't
match the originally set variable values and should not be trusted.

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Re: problem with nlsLM.....

akshay kulkarni
dear Ivan,
                   Thank you very much...You have been very helpful....

very many thanks for your time and effort....
yours sincerely,
AKSHAY M KULKARNI

________________________________
From: Ivan Krylov <[hidden email]>
Sent: Wednesday, March 20, 2019 4:08 PM
To: akshay kulkarni
Cc: R help Mailing list
Subject: Re: [R] problem with nlsLM.....

On Wed, 20 Mar 2019 09:43:11 +0000
akshay kulkarni <[hidden email]> wrote:

> But doesn't removing some of the parameters reduce the precision of
> the relationship between the response variable and the
> predictors(inefficient estimates of the coefficients)?

No, it doesn't, since there is already more variables in the formula
than it has relationships between response and predictors.

Let me offer you an example. Suppose you have a function y(x) = a*b*x +
c. Let's try to simulate some data and then fit it:

# choose according to your taste
a <- ...
b <- ...
c <- ...

# simulate model data
abc <- data.frame(x = runif(100))
abc$y <- a*b*abc$x + c
# add some normally distributed noise
abc$y <- abc$y + rnorm(100, 0, 0.01)

Now try to fit formula y ~ a*b*x + c using data in data frame abc. Do
you get any results? Do they match the values you have originally
set?[*]

Then try a formula with the ambiguity removed: y ~ d*x + c. Do you get a
result? Does the obtained d match a*b you had originally set?

Note that for the d you obtained you can get an infinite amount of
(a,b) tuples equally satisfying the equation d = a*b and the original
regression problem, unless you constrain a or b.

--
Best regards,
Ivan

[*] Using R, I couldn't, but the nonlinear solver in gnuplot is
sometimes able to give *a* result for such a degenerate problem when
data is sufficiently noisy. Of course, such a result usually doesn't
match the originally set variable values and should not be trusted.

        [[alternative HTML version deleted]]

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Re: Fw: problem with nlsLM.....

J C Nash
In reply to this post by akshay kulkarni
Of course, you might just try a more powerful approach. Duncan responded to the obvious issue earlier,
but the second problem seems to need the analytic derivatives of the nlsr package. Note that
nlsLM uses the SAME very simple forward difference derivative approximation for the Jacobian.
Optimization folk will generally say "singular Jacobian" and the singular values reported by
nlsr show how bad the case is here. Note that the nlsr output summary seems to imply that
the singular values are associated with specific parameters. My bad -- I wanted to keep
the summary tidy. The sv's apply to the problem in general, but there are as many of them as
parameters, so they fit on the page nicely where I've put them. The singularity leads to no
standard errors, of course.

JN

>> library(nlsr)
>> nonlin_modDH5 <- nlxb(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
> vn:[1] "HM1" "a"   "b"   "HM2" "A"   "w"   "HM3" "c"   "C"  
> no weights
>> nonlin_modDH5
> nlsr object: x
> residual sumsquares =  0.66987  on  680 observations
>     after  15    Jacobian and  20 function evaluations
>   name            coeff          SE       tstat      pval      gradient    JSingval  
> a              0.0929101            NA         NA         NA  -5.817e-07       42.97  
> b               0.541246            NA         NA         NA  -3.359e-07       7.043  
> A             0.00921275            NA         NA         NA    9.42e-08       0.984  
> w               0.545654            NA         NA         NA  -8.693e-09       0.195  
> c              -0.770181            NA         NA         NA    -5.2e-09     0.07464  
> C                0.60148            NA         NA         NA  -5.817e-07   7.567e-14  
>>



On 2019-03-20 4:20 a.m., akshay kulkarni wrote:

> dear members,
>                              I think Duncan's  solution is not working...I've checked the function again:
>
>> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) + (A*sin(w*HM3 + c) + C))
>> nonlin_modDH5 <- nls(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>> nonlin_modDH5 <- nlsLM(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>
>
> I've checked the Internet  for a method of getting the starting values, but they are not comprehensive....any resources for how to find the starting values?
>
>> dput(HM1)
> c(1.01484280343579, 0.977004104656811, 1.02448653468064, 1.01562102015533,
> 0.968359321714289, 0.99105072887847, 0.99384582083167, 1.00579414727333,
> 0.967593120445675, 1.04694148058855, 1.03832347454026, 0.971040156128456,
> 0.976967848281943, 0.990386686964446, 1.02257020722784, 0.994529161072552,
> 1.05361176882747, 0.988021514764882, 0.998909692369571, 1.04933819895509,
> 0.988354009297047, 0.878584020586706, 1.01438895222572, 1.01522203155842,
> 0.999979601913003, 0.975862934766315, 1.03948445565434, 1.02493339899053,
> 0.972267168470219, 0.971001051421653, 1.03066167719129, 0.967898970631469,
> 0.976606662010519, 0.990061873979734, 1.03398666689317, 1.05491455733517,
> 0.995997711923663, 0.991213393857838, 0.9903216916016, 1.06850794640807,
> 1.01345738121581, 0.877847194198055, 0.992124544661245, 1.00927272338804,
> 0.988196514422814, 0.987504905313256, 0.990274516132497, 1.01484624802751,
> 0.9800176904083, 0.969321725342799, 1.07407427451262, 1.02023130729371,
> 0.975827723120998, 0.988260886231393, 1.05116598585815, 0.977996171738906,
> 0.985118207891832, 0.98243346941999, 0.991380612684958, 1.00960741365585,
> 1.03062446605116, 1.04176358759364, 1.02494798972349, 1.04645592406625,
> 0.999998705820515, 0.972161829098397, 0.991313521356105, 1.0211561076261,
> 0.971257832217016, 1.00935026461508, 0.997140402835885, 1.00719609467891,
> 0.97483865743955, 0.987491380127905, 1.0643888147255, 1.00804564512905,
> 0.989366232219398, 0.942042386899482, 1.0140328534339, 0.98812919092058,
> 1.04584882138055, 0.970586857498767, 1.0098232311784, 1.02237732533005,
> 0.99306513943649, 0.963808401790738, 1.03577231048959, 1.00540101137939,
> 0.970458918298484, 0.973460294926879, 0.992676651187501, 1.04079931972326,
> 0.988928168736541, 0.986376934855979, 1.04117941173535, 1.01252015337269,
> 0.920578394310677, 0.98933093765412, 0.98116698970429, 0.995498900493761,
> 0.96463760451942, 1.1059686605887, 1.03366353582116, 0.981256131441973,
> 1.0152599452654, 0.995362080418312, 0.997276311537458, 0.943438355295013,
> 1.04025117202718, 1.03102737097366, 1.02096902615856, 1.02506560388494,
> 0.959364103040633, 0.987217067188943, 1.00454456275356, 1.03517749036747,
> 1.0148342900634, 0.974518005568706, 1.02515903830848, 1.00435361604303,
> 0.973400954462685, 0.981702992961201, 1.0155969247203, 1.01064154947471,
> 0.957131570492054, 0.88504625471737, 1.01849907662046, 1.01503806358198,
> 0.987004149653273, 0.974485857983481, 0.987572979379099, 1.0037529762248,
> 0.976026658183349, 0.983176178747953, 1.03812943411666, 1.01828203593425,
> 0.984139742592733, 1.00377243020886, 1.02118652563454, 1.01115776483144,
> 0.98387406692053, 0.985919715423209, 0.989550590454508, 1.01845373071313,
> 1.00394660346757, 0.949076679285431, 1.06094051230739, 0.981940494629297,
> 0.982812578115724, 0.929673163266673, 1.05359335975355, 1.03498999963467,
> 0.989218608625026, 0.994412680504384, 1.03162650392014, 1.04359975620911,
> 0.96938669600183, 0.987150178669349, 0.990503131271205, 1.01120231626779,
> 0.977334529946535, 0.966013274319284, 1.04966722359666, 1.04745672849702,
> 0.960799643211444, 0.866626315091455, 1.01316731305781, 1.02919067036911,
> 0.988567285725018, 0.978702298855537, 1.0186604054056, 1.01398864226215,
> 0.985492431198792, 1.03982326960775, 1.02287049736119, 1.02357106122821,
> 0.977519287403578, 0.993818572820706, 1.01139405704906, 1.02683235508704,
> 0.943370973180458, 0.970153991236375, 0.970746163945049, 1.00996886938921,
> 0.970599365922215, 1.03230385881209, 1.02072718512322, 1.0161024503161,
> 0.987718207880278, 0.987512809150475, 1.04108232940019, 1.0153758143276,
> 1.03916475100072, 0.959776942350879, 1.01739288375256, 1.05438479662213,
> 0.970479916939806, 0.979826128477137, 0.982800613999074, 0.977050857426291,
> 0.977474025608337, 0.972905352371901, 1.02191626900472, 1.02737043327345,
> 0.987565251408109, 0.93131925605196, 0.991469304659888, 0.949708678221412,
> 0.948662234856045, 0.938158485016718, 1.02342896552764, 1.01869899424866,
> 1.01244484998263, 0.990637832491416, 1.04591108144934, 0.951244278685872,
> 0.977265525807748, 0.942206977147219, 1.09440128638859, 1.02935716108991,
> 0.978428723706514, 1.08589930790608, 1.0015813231405, 1.07512822987239,
> 0.976356469419301, 0.972775004620642, 1.00954673593815, 0.992551447194369,
> 0.985819189451289, 0.975310750212573, 1.05999783258221, 1.07815688799274,
> 0.959648862885262, 1.03418767541976, 1.07207529899183, 1.04949264142055,
> 0.966872387358102, 0.963564695272614, 1.01310836328613, 0.996622968927382,
> 1.00202486063801, 0.948215304098709, 1.02845448262427, 0.987124642586768,
> 0.999269328077687, 0.964674178364545, 0.991913103743918, 0.970556592778505,
> 0.979495816235886, 0.949160643807912, 0.999611005677085, 1.01697727168167,
> 0.966362681248743, 0.938703012004999, 1.01394761650376, 1.011369485146,
> 0.975929406359934, 0.97111480362557, 1.0581474202687, 1.03965938660391,
> 0.990447147628825, 0.967286377976687, 0.968162001740811, 1.00573452177125,
> 0.974628505381728, 0.983183640545729, 1.08185959707149, 0.976533333333333,
> 0.932582771965921, 1.02205682784386, 1.02162002635618, 1.00634335266967,
> 1.03045302554561, 1.05015537832235, 1.0238800474742, 1.0151188247761,
> 0.951176244898346, 0.937999477475998, 0.986664690881963, 1.00889938014614,
> 0.972546365957459, 0.937560562249233, 0.953227957137494, 0.964284336810156,
> 0.977909815090698, 0.988068102557503, 1.04429493287078, 1.01803069781624,
> 0.957632272450841, 0.971991500790857, 0.98209395086599, 1.02815448263624,
> 0.947227198465786, 0.941538843776026, 1.05745885548346, 1.01671992454154,
> 0.983992269183969, 0.951511089781983, 1.0110983382201, 1.00912039359684,
> 1.14741321154181, 0.985344569298617, 0.986725720078838, 1.03117619992574,
> 1.21391930217405, 0.997217690041687, 1.07387278335981, 1.02108776576674,
> 1.10313006343474, 1.10905015571117, 1.03999508931616, 1.03976033898847,
> 1.02524837903366, 0.984818521675578, 1.04179889608739, 0.975154632188378,
> 0.969607959579886, 1.02781726918172, 0.999963290601752, 1.01515310515696,
> 1.00520167771997, 0.984550628692601, 1.01902810624565, 1.00685219196689,
> 0.982173452231062, 0.999629285915004, 0.970558368481076, 0.995716235126211,
> 1.01314341676203, 1.02075675497478, 1.01128668171558, 1.05844449406008,
> 1.02510935720045, 0.983720171349822, 0.844396082698583, 1.05810846093782,
> 0.99048977314373, 0.971262209252963, 0.968530578900656, 1.04638759101296,
> 1.1799017507928, 0.898448146333193, 1.01160891745375, 1.01132611427775,
> 0.973840647644128, 1.04683923857471, 0.990344793003927, 1.0068683076688,
> 0.992798241837764, 0.99368375299494, 1.01200003722717, 1.0294016781563,
> 1.01547984207734, 1.04807175686468, 0.981737356425208, 1.06169292627851,
> 1.00518291863648, 0.976808854545251, 0.999321592083517, 0.97527078961014,
> 0.904376968942001, 0.987807226191329, 1.0350293488462, 1.02802944623052,
> 0.963692701708712, 0.998027399233401, 1.01762747994825, 1.0361812777154,
> 0.988298193013531, 1.04174794125517, 0.970839140069407, 1.0011387710754,
> 0.961691863428911, 1.02272120871699, 1.00519180537531, 0.999331326197155,
> 0.988055521308447, 1.00078630058504, 1.03915754537212, 0.971719181925134,
> 0.947846525645955, 0.955178087494908, 1.0019107391621, 1.01160524115586,
> 0.984958748948305, 0.967579294820259, 1.00552471207069, 1.04518978636097,
> 1.01810784117456, 0.993552343701494, 1.03535983577691, 0.982973964981781,
> 0.976175417305875, 0.971173421374951, 1.02622192154044, 1.06169292627851,
> 0.980316131595686, 0.98802873807095, 1.02522486330609, 1.01920704714191,
> 0.987730950969463, 0.972320506142784, 1.02950031603837, 1.01481027236895,
> 1.05066348592531, 0.941412086703545, 1.00426249434292, 0.997599618540051,
> 0.9727707483253, 0.987822018689403, 1.00496006171741, 1.00365488022554,
> 0.982021737672154, 0.946014836456916, 0.977232083157459, 1.04246004468132,
> 0.981228463109654, 0.971195860055527, 0.957569129533581, 0.987312165664453,
> 0.955583610052133, 0.991173211015633, 0.988434021882053, 0.94971029297998,
> 1.02260060019826, 0.986729432665294, 1.00044978044626, 1.00283798105021,
> 0.97876379280085, 0.948623031823408, 1.02317511219612, 1.02274213461243,
> 0.952138762776323, 0.974014522860041, 1.02874293788325, 1.02108205408977,
> 0.950582622331241, 0.986098109021331, 1.00098916530354, 1.05435029523389,
> 0.999921440746985, 0.964778188342829, 1.02087753056688, 1.0295810738595,
> 0.981181990342875, 0.94981916198993, 1.00828646284387, 1.01127872412662,
> 0.976192285243012, 0.95032633282931, 1.02884118341175, 1.01603776783957,
> 0.952947066257398, 0.988924105443658, 1.02727453593468, 1.02944328284254,
> 0.970561666318556, 0.982859453938812, 0.995886713501412, 1.04330648532137,
> 0.991844865771659, 0.99159250467833, 1.0332159700053, 0.993864541664301,
> 0.952716253755034, 0.960889912140351, 0.945669021854222, 0.985578531230794,
> 0.975053113912155, 0.999552576905685, 1.02526821151208, 1.00450298718514,
> 0.948193972002236, 0.99358065264941, 1.04855364680554, 0.982181523974446,
> 0.957570494265717, 0.989249667650167, 1.01928030167592, 0.977392376116208,
> 0.98989833978368, 0.950312758756191, 1.01179069272545, 1.03630963397181,
> 0.997439452068598, 0.981793158114732, 0.98560918506727, 1.0497573745934,
> 1.06729226949522, 0.987962794653476, 1.05655507163022, 1.01896087310511,
> 0.985627836611195, 1.03148215915346, 1.07268555224398, 1.00116324087534,
> 1.0353274520202, 0.965843323254256, 0.981731445105197, 1.01927876056569,
> 0.999117162412819, 0.984262994383997, 1.0058994935294, 1.03100765642408,
> 0.970000664632288, 0.974667000854514, 1.02683721041632, 1.00224180239507,
> 0.976488446884133, 0.989129579180466, 1.01111944121172, 1.01081759149299,
> 0.990822520380867, 0.951849309560414, 1.0533258785109, 1.02605855087945,
> 0.993203883495146, 1.01231349444546, 1.05040134097269, 0.988195090931879,
> 0.998324259112233, 0.955268105597824, 1.01179535756103, 1.00923640076821,
> 1.01018973127051, 0.97727388908476, 1.0163531272719, 1.03445181115965,
> 0.922709820364792, 1.05034425545701, 0.971929096725562, 1.01600521659268,
> 0.989307124797035, 0.966327502991904, 1.0148486097639, 1.00804453317389,
> 0.986688556832374, 1.01100519377607, 1.07022911567444, 1.01751745478207,
> 1.00395256916996, 1.02666291685486, 1.04306886282915, 0.976284796774505,
> 0.873912939480404, 0.985956634379203, 0.966005821428092, 1.01911378235808,
> 0.989971040708023, 0.948518684869746, 0.964578663605958, 1.00130118816861,
> 1.14681217078448, 0.989198271209915, 0.975831372743337, 0.87134009583841,
> 0.999320371256267, 1.04366317607907, 1.0941742251269, 1.04912747347822,
> 0.873169695550468, 0.9946703855144, 1.02687904508206, 1.01557590029223,
> 0.979039928979583, 0.978199700239161, 0.988038704848986, 0.944583404359117,
> 0.97831517569251, 0.9881631354812, 0.990553852862955, 1.05139702349736,
> 0.987315661692795, 0.973936995140175, 1.06038641236987, 1.0651525599012,
> 0.997891074790299, 0.979657304627723, 0.976638433723023, 0.981157058049022,
> 0.974773098042751, 1.04814547433216, 1.02327857487465, 1.01725074945885,
> 0.978364366421386, 1.02726007295919, 1.02997589351422, 1.0208112362723,
> 0.965389721633011, 1.01152741033802, 1.00352090319123, 0.981151102088714,
> 0.986010324656192, 0.97990659165448, 0.990503768576242, 0.985301104774446,
> 0.976384165205197, 0.983887706958686, 0.992885571845564, 0.985757622165226,
> 0.946874017209482, 0.990962963812142, 1.04319324396693, 1.07354098057006,
> 0.984597785175012, 0.97845443949194, 0.963960215777945, 1.06584156194012,
> 1.15623372835712, 0.94928101059165, 1.05512096334013, 1.00899261027951,
> 1.00760894032175, 0.989021593753401, 1.01442722705518, 1.04495346859144,
> 1.00095646335122, 0.958907195434402, 1.02045280866442, 1.00659622664478,
> 0.980590749755976, 1.01197688939839, 0.998468538022796, 1.040625279257,
> 0.934164344256759, 0.992160161968429, 0.998594942355232, 1.0532781805963,
> 0.974144103277039, 0.946253316413415, 1.00623102828924, 0.988787044999963,
> 0.987541467770869, 0.999140303075891, 1.05429436081972, 1.01617267860198,
> 1.17044068684611, 1.05663112916056, 1.01681282884482, 1.02872692218644,
> 1.00738468152863, 1.0222081131894, 1.01817103171172, 1.01222250929532,
> 0.97386289237006, 0.987701353725335, 1.00418624697519, 1.0183108729199,
> 0.956852868616883, 1.05440980534023, 1.01562402108019, 1.00083274000183,
> 0.945641654423597, 0.966367379950251, 1.02020174816679, 1.01151664724635,
> 0.940072052642951, 0.991027873457432, 0.984568985852697, 1.02373049431651,
> 0.985887377562884, 1.05498951399906, 0.98672427011774, 1.02570399134917,
> 1.06987898201568, 1.02648218241568, 0.971175525661877, 1.03211886190237,
> 0.992647697074835, 0.986388969656675, 1.02097305537141, 1.04518978636097,
> 0.987300416455667, 0.985182286987375, 1.02373433952904, 1.01376295257605,
> 0.992398195656577, 0.983765510339449, 1.06154013507033, 1.01312319643703
> )
>
>
>> dput(HM3)
> c(0.196622289085315, -0.0436031046458981, 0.000820037203846464,
> 0.392758326108918, 0.0445839547736504, 0.223969403187679, -0.070152338130932,
> 0.197471312884591, 0.0498910549108242, -0.620322188497616, 0.488037411730214,
> 0.0461937792271113, 0.0051066586622384, 0.413877974790452, -0.154637441642314,
> 0.229802002983503, 0.509904610124243, 0.126452664534026, 0.583037470359474,
> 0.470621010682895, -0.00578078533090776, -0.457074922218691,
> -0.0506356375273661, -0.767302691971105, -0.000822439375515026,
> -0.170080770417037, -0.0638533091192526, 0.218492017287635, -0.088210288825341,
> -0.389219867379597, 1.23355646106213, -1.78773586807154, 0.0117401974737664,
> -0.154882548871051, -2.11547741687211, -0.0638533091192526, -8.18190789706224,
> 0.282699360997887, 0.0383693451361519, -0.0638533091192526, 0.832793868600698,
> 0.894379977535782, -0.302329357989944, 0.573489523345283, -0.223540700260161,
> 0.229802002983503, 0.0239731624923989, 0.556201388601996, -0.112552578411774,
> -0.0438764575701239, 0.470621010682895, -4.92500180132548, 0.00309129784132211,
> 0.172407876390051, 0.229802002983503, 0.061799194890529, -0.000864150216291216,
> -0.0683834224257835, -1.42874041532869, 0.654009227783557, -0.620322188497616,
> 0.272034153890376, -0.00558557185457871, 0.229802002983503, -1.53118563423569e-05,
> 0.470621010682895, -0.736608540541868, -1.47747441666374, -0.00291475050109709,
> -1.56966101463791, 0.362334298169537, -1.41867286052432, -0.0062864890624406,
> -0.223090980170635, 0.229802002983503, 0, 0.0147973092972513,
> 0.757637727251206, 0.332227874554666, -0.620322188497616, -0.0638533091192526,
> -0.0638533091192526, 1.59477829009842, -0.247635059505396, -0.0638533091192526,
> 0.384123364351151, -0.909542286753924, -1.61643290186366, -0.00969493151693274,
> -0.0638533091192526, 0.335128597413319, 0.229802002983503, -3.36005853184083,
> -1.77772450660144, -0.223359097040026, -4.75082975711953, 0.545441153017038,
> -0.0181047188208818, 0.229802002983503, 0.718505786757723, -0.199075726922584,
> 0.0779145211225284, 0.0981591683724576, -0.0638533091192526,
> 0.771585236053205, -0.124873344480422, -0.143455357050914, -0.982192613814533,
> 0.840325570227771, -0.0639597669819456, 0.272309718636627, 0.470621010682895,
> 0.000149945344883234, 0.0179651303218593, 0.586470204231886,
> 1.12661379530659, 0.587538739887084, -0.389219867379597, 0.231816913894395,
> -2.32942853843292, 0.229802002983503, 0.470621010682895, 7.7137219916985,
> 3.14411367083572, 0.0371415305209129, 0.109258089720235, 0.0744990417275174,
> 0.978949955731298, 0.0046627432182197, 0.0778534035355464, -0.258075504911111,
> 0.428481177165365, 0.470621010682895, 0.0118295358531435, 0.601929630798962,
> 0.677233238299559, -0.620322188497616, 0.901650947775265, 0.229802002983503,
> 0.114385813284502, -0.287236365991277, 0.00362108510216535, 0.54836513813854,
> -0.360900820378516, 0.901102331983843, 0.303232126663717, -0.389219867379597,
> -0.389219867379597, -0.0638533091192526, 0.0172621246134547,
> -0.0638533091192526, 0.036790744537595, 0.0267711437261105, 0.229802002983503,
> 2.31402345437609, 0.229802002983503, -0.620322188497616, 0.470621010682895,
> -0.0638533091192526, 0, -0.0638533091192526, 0.683308812634821,
> 0.598730940691975, 0.229802002983503, 0.697360153425382, 0.00772365161682687,
> -4.74600966429007, 0.593589435095411, -0.152099577506429, 0.0064938252361837,
> -0.108940122157535, 0.0514396263381645, 1.10452888209261, 0.229802002983503,
> -0.0638533091192526, 0, 0.0362637715949993, -2.79167649513235,
> 0.0221979095188955, 0.122798837911884, -3.07802860403649, 0.887963749928486,
> -0.0638533091192526, -2.59080601794078, -0.0638533091192526,
> -0.0720032601992142, 0.188653367126112, -0.390900634446388, 0.00626262322890335,
> 0.10449726079375, 0.190234594305548, 0.229802002983503, 0.470621010682895,
> 0.441447072301397, 0.710575973379983, 0.424723018059659, 0.0242933124111495,
> -0.0638533091192526, 0.221839916416526, 0.470621010682895, -0.0108657434161119,
> 0.734169111179744, 0.465629851897761, 0.0252290916908343, -0.0261736586703599,
> 0.470621010682895, 0.00872967160561454, 0.820007851307147, -0.0638533091192526,
> 0.162601634884078, 0.15876421059534, 0.276805180633414, -0.386980840708979,
> 0.10228170638373, 0.0199458385530109, 0.470621010682895, 0.360613568422605,
> 0.0398196823378306, -0.389219867379597, -0.00269176292387149,
> 0.553663817786709, 0.00803266728255501, 0.470621010682895, -0.389219867379597,
> 0.0531985700905557, -0.00966390653254687, 0.948851286517476,
> 0, -0.0889303986002686, -0.289482205789089, 0.470621010682895,
> -0.620322188497616, -1.33981685878991, 0.0183827591576367, 0.011135182844728,
> -0.0638533091192526, 2.32607985155626, -0.138083520720171, 0.00471455153770216,
> 0.158058381916342, 0.00450504854498247, 0.0789824876681455, 0.0704699239866158,
> -2.19086590383429, 0.0809155162971262, -0.0638533091192526, 0.0329404929656065,
> 0.0281676116282727, -0.389219867379597, 0.012790919597435, -0.00704771101800125,
> 0.0403529797537856, 0.0558406758822688, 0.164832137315436, -0.165567865123191,
> -0.11103429903157, -0.0355024172803945, 9.48260587090123e-05,
> 0.470621010682895, -0.0495357136064147, 0.229802002983503, 0.0637860580674806,
> -0.620322188497616, -0.0226657756569641, -0.389219867379597,
> -0.316625748304681, 0.229802002983503, 0, 0.364124775102099,
> -0.0310987139252845, 0.492424057922772, -0.224535737231339, -0.133435578504748,
> 0.229802002983503, 0.184389526960601, -3.97960671764283, 0.223470481648489,
> -0.620322188497616, 1.57339312469411, -3.97007919079201, 0.028292556618311,
> -0.0208634007145496, 0.253055735744466, -0.0638533091192526,
> -1.93884051014153, 0.0301349400625612, -0.0638533091192526, 0.000295057056987376,
> 0, 0.286160612098488, 0.101207913446722, 0.374419900839882, -0.106217410937095,
> 0.229802002983503, 2.31067381143729, 2.09201022549168, 0.00385297198146129,
> 0.0512938203984638, -1.74049624153457, 0.426702921643898, -0.0638533091192526,
> 0.018935028523515, 0.23126745462646, 0.00767373530818363, 0.229802002983503,
> 3.84024898682852, -0.0638533091192526, 0.389584248100691, 0.0449003257302091,
> 1.02228506672615, 0.229802002983503, -0.0638533091192526, -0.0829134652872339,
> 3.50596545804498, -0.389219867379597, 0.9170368554337, -0.0638533091192526,
> 0.393117195150408, 0.228802985129773, 0.380899253549764, 0.232617830598094,
> -0.389219867379597, 0.0459161417216973, 1.31722638132631, -0.151301202089443,
> -0.116990991708309, 0.927131393095724, 0.287762794679405, 0.53782819173468,
> -0.389219867379597, 0, 0.778671412967675, -0.389219867379597,
> -0.0683403974328925, 0, 0.229802002983503, 0.473035927661744,
> -0.620322188497616, 0.994155024290692, 0.229802002983503, 0.470621010682895,
> -0.194201456791898, 0.735175033053973, 0, 8.3894395202509, -2.71421951365695,
> 1.63476338054133, 0, -0.358533993792374, -0.180900357181005,
> 0.464964929465083, 0.828110705634323, 0.290014059095831, 0.0265807736784206,
> 0.913325706925416, -0.620322188497616, 0.482569085367064, 0,
> 0.999426501553857, -0.0638533091192526, -0.894190901951266, 0.0608976332239153,
> -0.389219867379597, 0.000920175777920006, 0.0444241832790267,
> -0.389219867379597, 1.77775509259178, 0.00731201645980821, 0,
> -0.620322188497616, 0.229802002983503, 5.07464022484589, 1.48600912617683,
> 0.037030406316896, 5.19861898546793, 0.614580107047209, 0.140671508556198,
> -0.0638533091192526, 0.470621010682895, 1.44599607789379, 0.229802002983503,
> 0.0337669838054181, -0.339507807487096, -0.227604499761264, -0.0126140839785072,
> 0.329583322374931, -0.348480271857373, 0.229802002983503, 0.220851780815745,
> -0.00466286305694716, 0.470621010682895, 1.94004188027446, 0.00769634829635375,
> 0.229802002983503, 0.229802002983503, -0.0638533091192526, 0.00196933273638366,
> 0.362886861307995, -0.010895368171175, 0.00517992475169629, 0.0121875763733234,
> 0.229802002983503, -0.389219867379597, -0.000556322059116982,
> 0.229802002983503, 0.00260711445755056, -0.58609877956866, 0,
> 0.173845846564558, 0.050042474875399, -0.608356727049781, 1.29406201978428,
> -0.0207947874733201, 0.00589643228835565, 0.720668560955405,
> -0.0638533091192526, 0.0325689306223551, -0.620322188497616,
> -0.389219867379597, -0.0638533091192526, -0.620322188497616,
> 0.0384932081048732, -0.0349291605765668, 0.00798065241516178,
> -0.134723236994354, -0.0638533091192526, 0.281152548562933, 0.785134308633961,
> 0.00911501343278954, 0.0290625580516694, 0.229802002983503, 0.0364502381509222,
> 0.0200267754432485, 0.470621010682895, -2.30392713054924, 0.0133977174531081,
> 0.0313438687192373, -0.453629796591262, -0.444456614744397, 0.470621010682895,
> -0.00419309963021769, 0.229802002983503, 0.781666390558601, 0.263209713357162,
> -0.00288720581609739, 0.0157812058748433, 0.377266856994834,
> 0.211102246795362, -0.0244674117557579, 0.502934845080243, 0.470621010682895,
> -0.620322188497616, 0.119508324298399, 0.68156448564652, -0.389219867379597,
> 0.344388633359758, 0.541519544456095, 0.348203898915403, -0.617865773083779,
> -0.620322188497616, -0.389219867379597, -0.630469325697215, -0.389219867379597,
> -0.00185171176208817, 0.470621010682895, -0.0638533091192526,
> 0.0384110765023374, -0.476268477810135, 0.0275459003440332, -0.377613497242655,
> 0.480114279740138, 0.208177472628734, 0.0388621060580001, 0.314810488790625,
> -0.389219867379597, 0.622343433254446, 0.229802002983503, 0,
> 0.0116064532794852, 0.640880718848922, 0.0159256710348949, 0.0294218642201815,
> -0.11922554805783, 0.470621010682895, 0.0719964703043177, 0.176673405801693,
> -0.216160070123196, -0.620322188497616, 0.263287565296575, 0.334468720690456,
> -0.389219867379597, -0.389219867379597, 0, -0.0638533091192526,
> -0.0638533091192526, 0.328005934417359, 1.98345092754637, -0.0638533091192526,
> -0.322569885555602, 0.343583272797935, -0.0131035846556284, -0.0638533091192526,
> 0.332049938227108, 0.229802002983503, -0.184471568949188, 0.0738083137554319,
> 0.295094806935004, 2.00072915211246, 0.0524057350651284, -0.394994587233133,
> 0.11082820395266, 0.0470433275207449, -0.0244714107059427, -0.19392136026585,
> -0.389219867379597, 1.54447845601115, 0, 1.50461847711675, 0.470621010682895,
> -2.19230689436821, -2.15372326763204, 0.0602893383146939, -0.350076259362557,
> -0.389219867379597, -0.0491349223300808, -0.347427839125497,
> -0.125793370606986, -0.389219867379597, 0.218492017287635, -0.389219867379597,
> -2.19203559916863, 0.470621010682895, -0.0756276343860818, -0.620322188497616,
> 0.039618360679447, 0.229802002983503, 0.342229794086586, 1.22342842687211,
> -0.0638533091192526, -0.389219867379597, 0, -0.121360237566266,
> 0.470621010682895, 0.203522177713664, -2.66081323726908, 0.164981226545032,
> 0.208989371873146, 0.178977127763616, 0.00534834375159016, 0.0259183781780568,
> -0.0638533091192526, -0.620322188497616, 3.77342257919255, 0.077694414226668,
> -0.0638533091192526, -2.76001789665019, 0.93498899883197, 0.229802002983503,
> 0.229802002983503, -0.189889234701173, -0.878128024239223, 0.229802002983503,
> -0.971246200836784, 0.00238384747429768, -0.620322188497616,
> 0.440261892604354, 3.76354782684596, 0.470621010682895, 3.86172131986622,
> 0.575298756860043, 12.9023010859722, 0.470621010682895, 0.378503973417637,
> 0.474891582933229, 0, -0.389219867379597, 0.520087612011093,
> 0.00160600701629905, -1.70670322718237, 0.229802002983503, -0.0349116284595025,
> -0.389219867379597, 0.229802002983503, -0.0638533091192526, 0.0138641669555371,
> -1.76999545498462, 0.470621010682895, -0.0638533091192526, 0.470621010682895,
> 0.568835467760944, 0.196290116211879, 0.00657090150018764, 1.15108667563071,
> 0.229802002983503, 1.39616453144586, -0.0847185084207546, -0.063387618790934,
> 0.470621010682895, 0.111214507604595, -0.0859882232308096, 0.229802002983503,
> 0.00453833599472268, 0.334589717726727, 0.470621010682895, -0.390600326717378,
> 0.254343160688718, -0.951335732362531, -0.0638533091192526, -0.389219867379597,
> 0.185995171841162, -0.0638533091192526, 0.0161178324221534, -0.702070844948495,
> -0.466435025160672, 0.470621010682895, 0.470621010682895, -0.383876151221017,
> -0.0638533091192526, -0.0638533091192526, 0.470621010682895,
> -0.389219867379597, -0.0123155655016255, -0.386709341729674,
> 0.229802002983503, -0.620322188497616, 0.187872106293471, 1.0998198175547,
> 0.229802002983503, -0.0111456829865045, -0.620322188497616, -0.620322188497616,
> 0.0484181771965215, 0.29713373197861, 1.03416257683562, 0.470621010682895,
> -1.14286511426204, -0.620322188497616, -0.620322188497616, 3.57860778510592,
> 0.229802002983503, -0.62520946847561, 0.290707988341369, 0.0924839451700828,
> -0.292199030679553, 0.275232409230032, -0.0884019891746591, -0.613845322077624,
> 0.470621010682895, 0.27263770155515, 0.229802002983503, 0.470621010682895,
> -0.616577036861926, -0.0638533091192526, 0.229802002983503, 0.103227620279027,
> 0.000325827725882409, -0.389219867379597, -0.0213033086486025,
> 1.1517268134498, -0.0027549233829829, -5.20036700047062, -0.389219867379597,
> 1.38009140442044, 0.188112123387496, -0.389219867379597, -0.0715310034898503,
> 0.229802002983503, -1.42113754281828, 0.0671280483594289, -0.866540256758301,
> -0.389219867379597, -0.389219867379597, 0.112435457850701, -0.162158911168545,
> 0.470621010682895, 0.470621010682895, 0.0211915910476394, -0.00153721649859708,
> -0.0638533091192526, -0.102776844490052)
>
>> dput(HM2)
> c(0.016497438441392, -0.0121005433105642, 0.0340103138875006,
> 0.0393128837416979, 0.144718671661076, 0.0775923152157973, 0.000894372752462123,
> 0.0129080340603581, -0.0022179741964203, 0.0540058630486591,
> 0.0383234745402589, -0.0142841740056228, -0.0113383036972718,
> 0.00764362896437113, 0.022570207227843, 0.0892462240318431, 0.060498120257716,
> 0.0432525311181986, 0.00222557516581898, 0.0493381989550945,
> -0.0116459907029527, -0.112480015005373, 0.0182691285457092,
> 0.0248597234825647, 0.00754701511666922, 0.0471476327719148,
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>
>
> very many thanks for your time and effort...
> yours sincerely
>
> AKSHAY M KULKARNI
>
>
> ________________________________________
> From: R-help <[hidden email]> on behalf of akshay kulkarni <[hidden email]>
> Sent: Wednesday, March 20, 2019 1:32 PM
> To: R help Mailing  list
> Subject: [R] problem with nlsLM.....
>
> dear members,
>                              Yesterday, Duncan identified a silly mistake in an nls call...in the list of starting values, one of the names of the variable was getting repeated. But I am experiencing the same problem again, with a different formula:
>
>> formulaDH3
> HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))
>
> here HM1 is the response variable, and HM2 and HM3 are predictors.....
>
>> nonlin_modDH3 <- nls(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>> nonlin_modDH3 <- nlsLM(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>
> I am using nlsLM function from the minpack.lm package which says that it will converge when nls fails with singular gradient matrix error...
>
>
> Is there again a silly mistake(pardon me again if there is one!), or is the problem serious? If it is serious, any pointers towards a solution?
>
> very many thanks for your time and effort....
> yours sincerely,
> AKSHAY M KULKARNI
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> 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.
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> 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.
>

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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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.
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Re: Fw: problem with nlsLM.....

akshay kulkarni
dear JN,
                nlsr seems to impress me....But I have posted a different but related problem I am facing with nls(please check the mail list of today(21/03/2019 - 5:45 Indian standard time)). Can nlsr help me in this case too?

very many thanks for your time and effort...
yours sincerely,
AKSHAY M KULKARNI

________________________________
From: J C Nash <[hidden email]>
Sent: Wednesday, March 20, 2019 6:15 PM
To: akshay kulkarni; R help Mailing list
Subject: Re: [R] Fw: problem with nlsLM.....

Of course, you might just try a more powerful approach. Duncan responded to the obvious issue earlier,
but the second problem seems to need the analytic derivatives of the nlsr package. Note that
nlsLM uses the SAME very simple forward difference derivative approximation for the Jacobian.
Optimization folk will generally say "singular Jacobian" and the singular values reported by
nlsr show how bad the case is here. Note that the nlsr output summary seems to imply that
the singular values are associated with specific parameters. My bad -- I wanted to keep
the summary tidy. The sv's apply to the problem in general, but there are as many of them as
parameters, so they fit on the page nicely where I've put them. The singularity leads to no
standard errors, of course.

JN

>> library(nlsr)
>> nonlin_modDH5 <- nlxb(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
> vn:[1] "HM1" "a"   "b"   "HM2" "A"   "w"   "HM3" "c"   "C"
> no weights
>> nonlin_modDH5
> nlsr object: x
> residual sumsquares =  0.66987  on  680 observations
>     after  15    Jacobian and  20 function evaluations
>   name            coeff          SE       tstat      pval      gradient    JSingval
> a              0.0929101            NA         NA         NA  -5.817e-07       42.97
> b               0.541246            NA         NA         NA  -3.359e-07       7.043
> A             0.00921275            NA         NA         NA    9.42e-08       0.984
> w               0.545654            NA         NA         NA  -8.693e-09       0.195
> c              -0.770181            NA         NA         NA    -5.2e-09     0.07464
> C                0.60148            NA         NA         NA  -5.817e-07   7.567e-14
>>



On 2019-03-20 4:20 a.m., akshay kulkarni wrote:

> dear members,
>                              I think Duncan's  solution is not working...I've checked the function again:
>
>> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) + (A*sin(w*HM3 + c) + C))
>> nonlin_modDH5 <- nls(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>> nonlin_modDH5 <- nlsLM(formulaDH5, start = list(a = 0.43143, b = 0.68173,A = 0.09,w = 0.8,c = 0.01,C = 0.94))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>
>
> I've checked the Internet  for a method of getting the starting values, but they are not comprehensive....any resources for how to find the starting values?
>
>> dput(HM1)
> c(1.01484280343579, 0.977004104656811, 1.02448653468064, 1.01562102015533,
> 0.968359321714289, 0.99105072887847, 0.99384582083167, 1.00579414727333,
> 0.967593120445675, 1.04694148058855, 1.03832347454026, 0.971040156128456,
> 0.976967848281943, 0.990386686964446, 1.02257020722784, 0.994529161072552,
> 1.05361176882747, 0.988021514764882, 0.998909692369571, 1.04933819895509,
> 0.988354009297047, 0.878584020586706, 1.01438895222572, 1.01522203155842,
> 0.999979601913003, 0.975862934766315, 1.03948445565434, 1.02493339899053,
> 0.972267168470219, 0.971001051421653, 1.03066167719129, 0.967898970631469,
> 0.976606662010519, 0.990061873979734, 1.03398666689317, 1.05491455733517,
> 0.995997711923663, 0.991213393857838, 0.9903216916016, 1.06850794640807,
> 1.01345738121581, 0.877847194198055, 0.992124544661245, 1.00927272338804,
> 0.988196514422814, 0.987504905313256, 0.990274516132497, 1.01484624802751,
> 0.9800176904083, 0.969321725342799, 1.07407427451262, 1.02023130729371,
> 0.975827723120998, 0.988260886231393, 1.05116598585815, 0.977996171738906,
> 0.985118207891832, 0.98243346941999, 0.991380612684958, 1.00960741365585,
> 1.03062446605116, 1.04176358759364, 1.02494798972349, 1.04645592406625,
> 0.999998705820515, 0.972161829098397, 0.991313521356105, 1.0211561076261,
> 0.971257832217016, 1.00935026461508, 0.997140402835885, 1.00719609467891,
> 0.97483865743955, 0.987491380127905, 1.0643888147255, 1.00804564512905,
> 0.989366232219398, 0.942042386899482, 1.0140328534339, 0.98812919092058,
> 1.04584882138055, 0.970586857498767, 1.0098232311784, 1.02237732533005,
> 0.99306513943649, 0.963808401790738, 1.03577231048959, 1.00540101137939,
> 0.970458918298484, 0.973460294926879, 0.992676651187501, 1.04079931972326,
> 0.988928168736541, 0.986376934855979, 1.04117941173535, 1.01252015337269,
> 0.920578394310677, 0.98933093765412, 0.98116698970429, 0.995498900493761,
> 0.96463760451942, 1.1059686605887, 1.03366353582116, 0.981256131441973,
> 1.0152599452654, 0.995362080418312, 0.997276311537458, 0.943438355295013,
> 1.04025117202718, 1.03102737097366, 1.02096902615856, 1.02506560388494,
> 0.959364103040633, 0.987217067188943, 1.00454456275356, 1.03517749036747,
> 1.0148342900634, 0.974518005568706, 1.02515903830848, 1.00435361604303,
> 0.973400954462685, 0.981702992961201, 1.0155969247203, 1.01064154947471,
> 0.957131570492054, 0.88504625471737, 1.01849907662046, 1.01503806358198,
> 0.987004149653273, 0.974485857983481, 0.987572979379099, 1.0037529762248,
> 0.976026658183349, 0.983176178747953, 1.03812943411666, 1.01828203593425,
> 0.984139742592733, 1.00377243020886, 1.02118652563454, 1.01115776483144,
> 0.98387406692053, 0.985919715423209, 0.989550590454508, 1.01845373071313,
> 1.00394660346757, 0.949076679285431, 1.06094051230739, 0.981940494629297,
> 0.982812578115724, 0.929673163266673, 1.05359335975355, 1.03498999963467,
> 0.989218608625026, 0.994412680504384, 1.03162650392014, 1.04359975620911,
> 0.96938669600183, 0.987150178669349, 0.990503131271205, 1.01120231626779,
> 0.977334529946535, 0.966013274319284, 1.04966722359666, 1.04745672849702,
> 0.960799643211444, 0.866626315091455, 1.01316731305781, 1.02919067036911,
> 0.988567285725018, 0.978702298855537, 1.0186604054056, 1.01398864226215,
> 0.985492431198792, 1.03982326960775, 1.02287049736119, 1.02357106122821,
> 0.977519287403578, 0.993818572820706, 1.01139405704906, 1.02683235508704,
> 0.943370973180458, 0.970153991236375, 0.970746163945049, 1.00996886938921,
> 0.970599365922215, 1.03230385881209, 1.02072718512322, 1.0161024503161,
> 0.987718207880278, 0.987512809150475, 1.04108232940019, 1.0153758143276,
> 1.03916475100072, 0.959776942350879, 1.01739288375256, 1.05438479662213,
> 0.970479916939806, 0.979826128477137, 0.982800613999074, 0.977050857426291,
> 0.977474025608337, 0.972905352371901, 1.02191626900472, 1.02737043327345,
> 0.987565251408109, 0.93131925605196, 0.991469304659888, 0.949708678221412,
> 0.948662234856045, 0.938158485016718, 1.02342896552764, 1.01869899424866,
> 1.01244484998263, 0.990637832491416, 1.04591108144934, 0.951244278685872,
> 0.977265525807748, 0.942206977147219, 1.09440128638859, 1.02935716108991,
> 0.978428723706514, 1.08589930790608, 1.0015813231405, 1.07512822987239,
> 0.976356469419301, 0.972775004620642, 1.00954673593815, 0.992551447194369,
> 0.985819189451289, 0.975310750212573, 1.05999783258221, 1.07815688799274,
> 0.959648862885262, 1.03418767541976, 1.07207529899183, 1.04949264142055,
> 0.966872387358102, 0.963564695272614, 1.01310836328613, 0.996622968927382,
> 1.00202486063801, 0.948215304098709, 1.02845448262427, 0.987124642586768,
> 0.999269328077687, 0.964674178364545, 0.991913103743918, 0.970556592778505,
> 0.979495816235886, 0.949160643807912, 0.999611005677085, 1.01697727168167,
> 0.966362681248743, 0.938703012004999, 1.01394761650376, 1.011369485146,
> 0.975929406359934, 0.97111480362557, 1.0581474202687, 1.03965938660391,
> 0.990447147628825, 0.967286377976687, 0.968162001740811, 1.00573452177125,
> 0.974628505381728, 0.983183640545729, 1.08185959707149, 0.976533333333333,
> 0.932582771965921, 1.02205682784386, 1.02162002635618, 1.00634335266967,
> 1.03045302554561, 1.05015537832235, 1.0238800474742, 1.0151188247761,
> 0.951176244898346, 0.937999477475998, 0.986664690881963, 1.00889938014614,
> 0.972546365957459, 0.937560562249233, 0.953227957137494, 0.964284336810156,
> 0.977909815090698, 0.988068102557503, 1.04429493287078, 1.01803069781624,
> 0.957632272450841, 0.971991500790857, 0.98209395086599, 1.02815448263624,
> 0.947227198465786, 0.941538843776026, 1.05745885548346, 1.01671992454154,
> 0.983992269183969, 0.951511089781983, 1.0110983382201, 1.00912039359684,
> 1.14741321154181, 0.985344569298617, 0.986725720078838, 1.03117619992574,
> 1.21391930217405, 0.997217690041687, 1.07387278335981, 1.02108776576674,
> 1.10313006343474, 1.10905015571117, 1.03999508931616, 1.03976033898847,
> 1.02524837903366, 0.984818521675578, 1.04179889608739, 0.975154632188378,
> 0.969607959579886, 1.02781726918172, 0.999963290601752, 1.01515310515696,
> 1.00520167771997, 0.984550628692601, 1.01902810624565, 1.00685219196689,
> 0.982173452231062, 0.999629285915004, 0.970558368481076, 0.995716235126211,
> 1.01314341676203, 1.02075675497478, 1.01128668171558, 1.05844449406008,
> 1.02510935720045, 0.983720171349822, 0.844396082698583, 1.05810846093782,
> 0.99048977314373, 0.971262209252963, 0.968530578900656, 1.04638759101296,
> 1.1799017507928, 0.898448146333193, 1.01160891745375, 1.01132611427775,
> 0.973840647644128, 1.04683923857471, 0.990344793003927, 1.0068683076688,
> 0.992798241837764, 0.99368375299494, 1.01200003722717, 1.0294016781563,
> 1.01547984207734, 1.04807175686468, 0.981737356425208, 1.06169292627851,
> 1.00518291863648, 0.976808854545251, 0.999321592083517, 0.97527078961014,
> 0.904376968942001, 0.987807226191329, 1.0350293488462, 1.02802944623052,
> 0.963692701708712, 0.998027399233401, 1.01762747994825, 1.0361812777154,
> 0.988298193013531, 1.04174794125517, 0.970839140069407, 1.0011387710754,
> 0.961691863428911, 1.02272120871699, 1.00519180537531, 0.999331326197155,
> 0.988055521308447, 1.00078630058504, 1.03915754537212, 0.971719181925134,
> 0.947846525645955, 0.955178087494908, 1.0019107391621, 1.01160524115586,
> 0.984958748948305, 0.967579294820259, 1.00552471207069, 1.04518978636097,
> 1.01810784117456, 0.993552343701494, 1.03535983577691, 0.982973964981781,
> 0.976175417305875, 0.971173421374951, 1.02622192154044, 1.06169292627851,
> 0.980316131595686, 0.98802873807095, 1.02522486330609, 1.01920704714191,
> 0.987730950969463, 0.972320506142784, 1.02950031603837, 1.01481027236895,
> 1.05066348592531, 0.941412086703545, 1.00426249434292, 0.997599618540051,
> 0.9727707483253, 0.987822018689403, 1.00496006171741, 1.00365488022554,
> 0.982021737672154, 0.946014836456916, 0.977232083157459, 1.04246004468132,
> 0.981228463109654, 0.971195860055527, 0.957569129533581, 0.987312165664453,
> 0.955583610052133, 0.991173211015633, 0.988434021882053, 0.94971029297998,
> 1.02260060019826, 0.986729432665294, 1.00044978044626, 1.00283798105021,
> 0.97876379280085, 0.948623031823408, 1.02317511219612, 1.02274213461243,
> 0.952138762776323, 0.974014522860041, 1.02874293788325, 1.02108205408977,
> 0.950582622331241, 0.986098109021331, 1.00098916530354, 1.05435029523389,
> 0.999921440746985, 0.964778188342829, 1.02087753056688, 1.0295810738595,
> 0.981181990342875, 0.94981916198993, 1.00828646284387, 1.01127872412662,
> 0.976192285243012, 0.95032633282931, 1.02884118341175, 1.01603776783957,
> 0.952947066257398, 0.988924105443658, 1.02727453593468, 1.02944328284254,
> 0.970561666318556, 0.982859453938812, 0.995886713501412, 1.04330648532137,
> 0.991844865771659, 0.99159250467833, 1.0332159700053, 0.993864541664301,
> 0.952716253755034, 0.960889912140351, 0.945669021854222, 0.985578531230794,
> 0.975053113912155, 0.999552576905685, 1.02526821151208, 1.00450298718514,
> 0.948193972002236, 0.99358065264941, 1.04855364680554, 0.982181523974446,
> 0.957570494265717, 0.989249667650167, 1.01928030167592, 0.977392376116208,
> 0.98989833978368, 0.950312758756191, 1.01179069272545, 1.03630963397181,
> 0.997439452068598, 0.981793158114732, 0.98560918506727, 1.0497573745934,
> 1.06729226949522, 0.987962794653476, 1.05655507163022, 1.01896087310511,
> 0.985627836611195, 1.03148215915346, 1.07268555224398, 1.00116324087534,
> 1.0353274520202, 0.965843323254256, 0.981731445105197, 1.01927876056569,
> 0.999117162412819, 0.984262994383997, 1.0058994935294, 1.03100765642408,
> 0.970000664632288, 0.974667000854514, 1.02683721041632, 1.00224180239507,
> 0.976488446884133, 0.989129579180466, 1.01111944121172, 1.01081759149299,
> 0.990822520380867, 0.951849309560414, 1.0533258785109, 1.02605855087945,
> 0.993203883495146, 1.01231349444546, 1.05040134097269, 0.988195090931879,
> 0.998324259112233, 0.955268105597824, 1.01179535756103, 1.00923640076821,
> 1.01018973127051, 0.97727388908476, 1.0163531272719, 1.03445181115965,
> 0.922709820364792, 1.05034425545701, 0.971929096725562, 1.01600521659268,
> 0.989307124797035, 0.966327502991904, 1.0148486097639, 1.00804453317389,
> 0.986688556832374, 1.01100519377607, 1.07022911567444, 1.01751745478207,
> 1.00395256916996, 1.02666291685486, 1.04306886282915, 0.976284796774505,
> 0.873912939480404, 0.985956634379203, 0.966005821428092, 1.01911378235808,
> 0.989971040708023, 0.948518684869746, 0.964578663605958, 1.00130118816861,
> 1.14681217078448, 0.989198271209915, 0.975831372743337, 0.87134009583841,
> 0.999320371256267, 1.04366317607907, 1.0941742251269, 1.04912747347822,
> 0.873169695550468, 0.9946703855144, 1.02687904508206, 1.01557590029223,
> 0.979039928979583, 0.978199700239161, 0.988038704848986, 0.944583404359117,
> 0.97831517569251, 0.9881631354812, 0.990553852862955, 1.05139702349736,
> 0.987315661692795, 0.973936995140175, 1.06038641236987, 1.0651525599012,
> 0.997891074790299, 0.979657304627723, 0.976638433723023, 0.981157058049022,
> 0.974773098042751, 1.04814547433216, 1.02327857487465, 1.01725074945885,
> 0.978364366421386, 1.02726007295919, 1.02997589351422, 1.0208112362723,
> 0.965389721633011, 1.01152741033802, 1.00352090319123, 0.981151102088714,
> 0.986010324656192, 0.97990659165448, 0.990503768576242, 0.985301104774446,
> 0.976384165205197, 0.983887706958686, 0.992885571845564, 0.985757622165226,
> 0.946874017209482, 0.990962963812142, 1.04319324396693, 1.07354098057006,
> 0.984597785175012, 0.97845443949194, 0.963960215777945, 1.06584156194012,
> 1.15623372835712, 0.94928101059165, 1.05512096334013, 1.00899261027951,
> 1.00760894032175, 0.989021593753401, 1.01442722705518, 1.04495346859144,
> 1.00095646335122, 0.958907195434402, 1.02045280866442, 1.00659622664478,
> 0.980590749755976, 1.01197688939839, 0.998468538022796, 1.040625279257,
> 0.934164344256759, 0.992160161968429, 0.998594942355232, 1.0532781805963,
> 0.974144103277039, 0.946253316413415, 1.00623102828924, 0.988787044999963,
> 0.987541467770869, 0.999140303075891, 1.05429436081972, 1.01617267860198,
> 1.17044068684611, 1.05663112916056, 1.01681282884482, 1.02872692218644,
> 1.00738468152863, 1.0222081131894, 1.01817103171172, 1.01222250929532,
> 0.97386289237006, 0.987701353725335, 1.00418624697519, 1.0183108729199,
> 0.956852868616883, 1.05440980534023, 1.01562402108019, 1.00083274000183,
> 0.945641654423597, 0.966367379950251, 1.02020174816679, 1.01151664724635,
> 0.940072052642951, 0.991027873457432, 0.984568985852697, 1.02373049431651,
> 0.985887377562884, 1.05498951399906, 0.98672427011774, 1.02570399134917,
> 1.06987898201568, 1.02648218241568, 0.971175525661877, 1.03211886190237,
> 0.992647697074835, 0.986388969656675, 1.02097305537141, 1.04518978636097,
> 0.987300416455667, 0.985182286987375, 1.02373433952904, 1.01376295257605,
> 0.992398195656577, 0.983765510339449, 1.06154013507033, 1.01312319643703
> )
>
>
>> dput(HM3)
> c(0.196622289085315, -0.0436031046458981, 0.000820037203846464,
> 0.392758326108918, 0.0445839547736504, 0.223969403187679, -0.070152338130932,
> 0.197471312884591, 0.0498910549108242, -0.620322188497616, 0.488037411730214,
> 0.0461937792271113, 0.0051066586622384, 0.413877974790452, -0.154637441642314,
> 0.229802002983503, 0.509904610124243, 0.126452664534026, 0.583037470359474,
> 0.470621010682895, -0.00578078533090776, -0.457074922218691,
> -0.0506356375273661, -0.767302691971105, -0.000822439375515026,
> -0.170080770417037, -0.0638533091192526, 0.218492017287635, -0.088210288825341,
> -0.389219867379597, 1.23355646106213, -1.78773586807154, 0.0117401974737664,
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> 0.000149945344883234, 0.0179651303218593, 0.586470204231886,
> 1.12661379530659, 0.587538739887084, -0.389219867379597, 0.231816913894395,
> -2.32942853843292, 0.229802002983503, 0.470621010682895, 7.7137219916985,
> 3.14411367083572, 0.0371415305209129, 0.109258089720235, 0.0744990417275174,
> 0.978949955731298, 0.0046627432182197, 0.0778534035355464, -0.258075504911111,
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> -0.0638533091192526, 0.036790744537595, 0.0267711437261105, 0.229802002983503,
> 2.31402345437609, 0.229802002983503, -0.620322188497616, 0.470621010682895,
> -0.0638533091192526, 0, -0.0638533091192526, 0.683308812634821,
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> -4.74600966429007, 0.593589435095411, -0.152099577506429, 0.0064938252361837,
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> -0.0638533091192526, 0, 0.0362637715949993, -2.79167649513235,
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> -0.0720032601992142, 0.188653367126112, -0.390900634446388, 0.00626262322890335,
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> 0.0398196823378306, -0.389219867379597, -0.00269176292387149,
> 0.553663817786709, 0.00803266728255501, 0.470621010682895, -0.389219867379597,
> 0.0531985700905557, -0.00966390653254687, 0.948851286517476,
> 0, -0.0889303986002686, -0.289482205789089, 0.470621010682895,
> -0.620322188497616, -1.33981685878991, 0.0183827591576367, 0.011135182844728,
> -0.0638533091192526, 2.32607985155626, -0.138083520720171, 0.00471455153770216,
> 0.158058381916342, 0.00450504854498247, 0.0789824876681455, 0.0704699239866158,
> -2.19086590383429, 0.0809155162971262, -0.0638533091192526, 0.0329404929656065,
> 0.0281676116282727, -0.389219867379597, 0.012790919597435, -0.00704771101800125,
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> -0.11103429903157, -0.0355024172803945, 9.48260587090123e-05,
> 0.470621010682895, -0.0495357136064147, 0.229802002983503, 0.0637860580674806,
> -0.620322188497616, -0.0226657756569641, -0.389219867379597,
> -0.316625748304681, 0.229802002983503, 0, 0.364124775102099,
> -0.0310987139252845, 0.492424057922772, -0.224535737231339, -0.133435578504748,
> 0.229802002983503, 0.184389526960601, -3.97960671764283, 0.223470481648489,
> -0.620322188497616, 1.57339312469411, -3.97007919079201, 0.028292556618311,
> -0.0208634007145496, 0.253055735744466, -0.0638533091192526,
> -1.93884051014153, 0.0301349400625612, -0.0638533091192526, 0.000295057056987376,
> 0, 0.286160612098488, 0.101207913446722, 0.374419900839882, -0.106217410937095,
> 0.229802002983503, 2.31067381143729, 2.09201022549168, 0.00385297198146129,
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> 3.84024898682852, -0.0638533091192526, 0.389584248100691, 0.0449003257302091,
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> 3.50596545804498, -0.389219867379597, 0.9170368554337, -0.0638533091192526,
> 0.393117195150408, 0.228802985129773, 0.380899253549764, 0.232617830598094,
> -0.389219867379597, 0.0459161417216973, 1.31722638132631, -0.151301202089443,
> -0.116990991708309, 0.927131393095724, 0.287762794679405, 0.53782819173468,
> -0.389219867379597, 0, 0.778671412967675, -0.389219867379597,
> -0.0683403974328925, 0, 0.229802002983503, 0.473035927661744,
> -0.620322188497616, 0.994155024290692, 0.229802002983503, 0.470621010682895,
> -0.194201456791898, 0.735175033053973, 0, 8.3894395202509, -2.71421951365695,
> 1.63476338054133, 0, -0.358533993792374, -0.180900357181005,
> 0.464964929465083, 0.828110705634323, 0.290014059095831, 0.0265807736784206,
> 0.913325706925416, -0.620322188497616, 0.482569085367064, 0,
> 0.999426501553857, -0.0638533091192526, -0.894190901951266, 0.0608976332239153,
> -0.389219867379597, 0.000920175777920006, 0.0444241832790267,
> -0.389219867379597, 1.77775509259178, 0.00731201645980821, 0,
> -0.620322188497616, 0.229802002983503, 5.07464022484589, 1.48600912617683,
> 0.037030406316896, 5.19861898546793, 0.614580107047209, 0.140671508556198,
> -0.0638533091192526, 0.470621010682895, 1.44599607789379, 0.229802002983503,
> 0.0337669838054181, -0.339507807487096, -0.227604499761264, -0.0126140839785072,
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> -0.00466286305694716, 0.470621010682895, 1.94004188027446, 0.00769634829635375,
> 0.229802002983503, 0.229802002983503, -0.0638533091192526, 0.00196933273638366,
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> 0.229802002983503, -0.389219867379597, -0.000556322059116982,
> 0.229802002983503, 0.00260711445755056, -0.58609877956866, 0,
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> -0.00288720581609739, 0.0157812058748433, 0.377266856994834,
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> -0.389219867379597, 0.622343433254446, 0.229802002983503, 0,
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> -0.11922554805783, 0.470621010682895, 0.0719964703043177, 0.176673405801693,
> -0.216160070123196, -0.620322188497616, 0.263287565296575, 0.334468720690456,
> -0.389219867379597, -0.389219867379597, 0, -0.0638533091192526,
> -0.0638533091192526, 0.328005934417359, 1.98345092754637, -0.0638533091192526,
> -0.322569885555602, 0.343583272797935, -0.0131035846556284, -0.0638533091192526,
> 0.332049938227108, 0.229802002983503, -0.184471568949188, 0.0738083137554319,
> 0.295094806935004, 2.00072915211246, 0.0524057350651284, -0.394994587233133,
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> -0.389219867379597, 1.54447845601115, 0, 1.50461847711675, 0.470621010682895,
> -2.19230689436821, -2.15372326763204, 0.0602893383146939, -0.350076259362557,
> -0.389219867379597, -0.0491349223300808, -0.347427839125497,
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> -2.19203559916863, 0.470621010682895, -0.0756276343860818, -0.620322188497616,
> 0.039618360679447, 0.229802002983503, 0.342229794086586, 1.22342842687211,
> -0.0638533091192526, -0.389219867379597, 0, -0.121360237566266,
> 0.470621010682895, 0.203522177713664, -2.66081323726908, 0.164981226545032,
> 0.208989371873146, 0.178977127763616, 0.00534834375159016, 0.0259183781780568,
> -0.0638533091192526, -0.620322188497616, 3.77342257919255, 0.077694414226668,
> -0.0638533091192526, -2.76001789665019, 0.93498899883197, 0.229802002983503,
> 0.229802002983503, -0.189889234701173, -0.878128024239223, 0.229802002983503,
> -0.971246200836784, 0.00238384747429768, -0.620322188497616,
> 0.440261892604354, 3.76354782684596, 0.470621010682895, 3.86172131986622,
> 0.575298756860043, 12.9023010859722, 0.470621010682895, 0.378503973417637,
> 0.474891582933229, 0, -0.389219867379597, 0.520087612011093,
> 0.00160600701629905, -1.70670322718237, 0.229802002983503, -0.0349116284595025,
> -0.389219867379597, 0.229802002983503, -0.0638533091192526, 0.0138641669555371,
> -1.76999545498462, 0.470621010682895, -0.0638533091192526, 0.470621010682895,
> 0.568835467760944, 0.196290116211879, 0.00657090150018764, 1.15108667563071,
> 0.229802002983503, 1.39616453144586, -0.0847185084207546, -0.063387618790934,
> 0.470621010682895, 0.111214507604595, -0.0859882232308096, 0.229802002983503,
> 0.00453833599472268, 0.334589717726727, 0.470621010682895, -0.390600326717378,
> 0.254343160688718, -0.951335732362531, -0.0638533091192526, -0.389219867379597,
> 0.185995171841162, -0.0638533091192526, 0.0161178324221534, -0.702070844948495,
> -0.466435025160672, 0.470621010682895, 0.470621010682895, -0.383876151221017,
> -0.0638533091192526, -0.0638533091192526, 0.470621010682895,
> -0.389219867379597, -0.0123155655016255, -0.386709341729674,
> 0.229802002983503, -0.620322188497616, 0.187872106293471, 1.0998198175547,
> 0.229802002983503, -0.0111456829865045, -0.620322188497616, -0.620322188497616,
> 0.0484181771965215, 0.29713373197861, 1.03416257683562, 0.470621010682895,
> -1.14286511426204, -0.620322188497616, -0.620322188497616, 3.57860778510592,
> 0.229802002983503, -0.62520946847561, 0.290707988341369, 0.0924839451700828,
> -0.292199030679553, 0.275232409230032, -0.0884019891746591, -0.613845322077624,
> 0.470621010682895, 0.27263770155515, 0.229802002983503, 0.470621010682895,
> -0.616577036861926, -0.0638533091192526, 0.229802002983503, 0.103227620279027,
> 0.000325827725882409, -0.389219867379597, -0.0213033086486025,
> 1.1517268134498, -0.0027549233829829, -5.20036700047062, -0.389219867379597,
> 1.38009140442044, 0.188112123387496, -0.389219867379597, -0.0715310034898503,
> 0.229802002983503, -1.42113754281828, 0.0671280483594289, -0.866540256758301,
> -0.389219867379597, -0.389219867379597, 0.112435457850701, -0.162158911168545,
> 0.470621010682895, 0.470621010682895, 0.0211915910476394, -0.00153721649859708,
> -0.0638533091192526, -0.102776844490052)
>
>> dput(HM2)
> c(0.016497438441392, -0.0121005433105642, 0.0340103138875006,
> 0.0393128837416979, 0.144718671661076, 0.0775923152157973, 0.000894372752462123,
> 0.0129080340603581, -0.0022179741964203, 0.0540058630486591,
> 0.0383234745402589, -0.0142841740056228, -0.0113383036972718,
> 0.00764362896437113, 0.022570207227843, 0.0892462240318431, 0.060498120257716,
> 0.0432525311181986, 0.00222557516581898, 0.0493381989550945,
> -0.0116459907029527, -0.112480015005373, 0.0182691285457092,
> 0.0248597234825647, 0.00754701511666922, 0.0471476327719148,
> 0.0625841102244406, 0.0303698631973417, 0.00781124380799404,
> -0.0168950573556397, 0.0473901805181147, 0.00740505106540693,
> -0.0152639817574294, 0.00916841478852549, 0.0409730632910991,
> 0.0625866632066982, 0.00664005076885597, 0.0103946833001679,
> 0.0243728672440622, 0.0722309706115829, 0.0174699387206196, -0.088714507487996,
> -0.00504535647738195, 0.00927272338803572, -0.00707755664004029,
> 0.209001332673238, 0.0280975011236682, 0.0185107628508926, -0.0152895488479284,
> 0.0293166958720994, 0.0839281669393395, 0.0350355281775659, -0.0214691529922399,
> -0.00698847060167527, 0.069853381162298, 0.00880702674287574,
> 0.0440111442331915, 0.0576762465158807, 0.0180025302908308, 0.0104372279684426,
> 0.0399937793788931, 0.0484415593089806, 0.0359627022827372, 0.0610406756211853,
> 0.0400425377047152, 0.00783749255154968, 0.0033626359803225,
> 0.0253846624900874, 0.028956317299215, 0.0220327419434368, 0.0055491218235435,
> 0.021359789760335, 0.0053563472471669, 0.00770764128276149, 0.120606533813112,
> 0.0338481150043749, 0.00480652155775551, -0.0208260153687132,
> 0.0264346940874102, 0.0254170849175835, 0.0549828722223003, -0.0197153554154837,
> 0.0146319132316291, 0.0223773253300461, 0.046744336162787, 0.0439389840326429,
> 0.0730303072697907, 0.0157399337823011, 0.0189220332073482, 0.0332902036920697,
> -0.00629466834494764, 0.0674012192688983, 0.0200657055355507,
> 0.0101015050127517, 0.0425927774526406, 0.0282767872201141, 0.00648039037553307,
> 0.0515174537352363, 0.128342038159934, 0.0432170296083376, 0.01871577325763,
> 0.122157724187454, 0.0336635358211635, 0.0225239126708406, 0.0183439669703353,
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>
>
> very many thanks for your time and effort...
> yours sincerely
>
> AKSHAY M KULKARNI
>
>
> ________________________________________
> From: R-help <[hidden email]> on behalf of akshay kulkarni <[hidden email]>
> Sent: Wednesday, March 20, 2019 1:32 PM
> To: R help Mailing  list
> Subject: [R] problem with nlsLM.....
>
> dear members,
>                              Yesterday, Duncan identified a silly mistake in an nls call...in the list of starting values, one of the names of the variable was getting repeated. But I am experiencing the same problem again, with a different formula:
>
>> formulaDH3
> HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))
>
> here HM1 is the response variable, and HM2 and HM3 are predictors.....
>
>> nonlin_modDH3 <- nls(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>> nonlin_modDH3 <- nlsLM(formulaDH3, start = list(a = 0.43143, b = 0.68173,c = 0.02954))
> Error in nlsModel(formula, mf, start, wts) :
>   singular gradient matrix at initial parameter estimates
>
> I am using nlsLM function from the minpack.lm package which says that it will converge when nls fails with singular gradient matrix error...
>
>
> Is there again a silly mistake(pardon me again if there is one!), or is the problem serious? If it is serious, any pointers towards a solution?
>
> very many thanks for your time and effort....
> yours sincerely,
> AKSHAY M KULKARNI
>
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>
> ______________________________________________
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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.
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
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>

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______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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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.