Caros,boa noite,

estou tentando fazer um fit em um conjunto de dados mas ele me retorna o
erro:

Erro em nls(y ~ func(x, c, a, b), data = dados2, start = guess, trace =
TRUE) :
  fator de passos 0.000488281 reduzido abaixo de 'minFactor' de 0.000976562

Alguém, por favor, saberia me dizer como posso concertar isso?
O programa segue abaixo:
> require(lattice)
> l<-1
>
file<-read.table(paste0("/Users/bau/ProjetoU-Shape/AA.N_Novo_MomentosEstatísticosSemestre",l,".txt"),
header=TRUE)
> media<-file[3:389,2]
> media
  [1] 24117.500 13168.333 13861.667 18031.667 19581.667 18797.500 15147.500
16699.167 17911.667
 [10] 16430.000 15874.167 15470.000 16360.833 18240.000 15018.333 18155.833
14793.333 17348.333
 [19] 19142.500 21510.833 17417.500 17450.833 19335.833 14439.167 16995.000
14624.167 18152.500
 [28] 18240.000 16124.167 18489.167 14679.167 17181.667 18115.833 15302.500
13445.833 15159.167
 [37] 20161.667 15321.667 14252.500 15850.833 13745.000 13915.833 15810.833
13280.833 14680.833
 [46] 12119.167 12100.833 16263.333 13210.833 15323.333 12921.667 14374.167
12805.833 13955.000
 [55] 12920.833 12016.667 13795.000 14751.667 12735.833 12267.500 12896.667
11947.500 12723.333
 [64] 14574.167 12818.333 14187.500 11534.167 12134.167 10910.000 12596.667
11503.333 12515.833
 [73] 12709.167 14239.167 11495.000 13163.333 11990.000 10506.667 11984.167
10225.000 11447.500
 [82] 11966.667 12115.833 11273.333 10478.333 10375.000 12240.833 11178.333
10699.167 11474.167
 [91] 10075.000 12563.333 14194.167 13675.833 12290.000 11357.500 10956.667
12120.000 11663.333
[100] 11078.333 11371.667 10549.167 12766.667 14523.333 10948.333 10809.167
10772.500 11190.000
[109]  9748.333 14768.333 11070.000 10362.500 10942.500  9190.833 12433.333
10302.500 10838.333
[118] 12184.167  8899.167 10073.333 10393.333  9760.000 10390.833 11309.167
 9319.167  8183.333
[127]  9531.667  9843.333  8924.167 10070.833  8730.000 10109.167 10507.500
11991.667  8540.833
[136] 11396.667  9405.000  9284.167  9146.667  9640.000  8991.667  8352.500
 7969.167 10366.667
[145]  9319.167  8555.000  8578.333  7897.500  8881.667  8842.500  9579.167
 9022.500  9914.167
[154]  9933.333 10987.500  8835.000  8473.333  9029.167  8619.167  8782.500
 9901.667  8566.667
[163]  8490.833  7475.833  7510.833  8990.000  6865.833  6564.167  7418.333
 7356.667  7485.000
[172]  8482.500  9325.000  7989.167  7370.833  7921.667  8243.333  8005.833
 8551.667  9119.167
[181]  8760.000  7193.333  9693.333  7742.500 11112.500  7497.500  6685.000
 8441.667  7150.000
[190]  6189.167  6760.833  6255.833  8953.333  8413.333  7731.667  6546.667
 7179.167  8060.000
[199]  7023.333  9560.000  7071.667  7770.000  6640.000  5910.833  8896.667
 7938.333  7098.333
[208]  7282.500  7291.667  8557.500  7326.667  8196.667 12226.667  8380.000
 6513.333  9100.833
[217]  6303.333  7525.000  7297.500  7375.833  7224.167  7029.167  9325.833
 5923.333  7674.167
[226]  5882.500  8318.333  7263.333  7574.167  7268.333  7530.000  9355.833
 8654.167  7426.667
[235]  7794.167  7008.333  7974.167 10410.000  7181.667  7440.000  6345.000
 5503.333  6674.167
[244]  9780.000  7740.000  6981.667  5926.667  9352.500 10264.167  8406.667
 6532.500  6478.333
[253]  8561.667  8072.500  7700.000  7189.167  6443.333  8120.000  7190.833
 6520.833  7539.167
[262]  8013.333  7191.667  7660.000  7276.667  8233.333  7500.833  8582.500
 8019.167  7231.667
[271] 10179.167  8445.000  9302.500  7680.833  9114.167  8432.500  7475.000
 7779.167  8895.000
[280]  9171.667  9753.333  7490.000 10211.667 11380.000  8524.167  8077.500
10155.000  9406.667
[289] 11199.167  8286.667  9850.000 10032.500  9740.833  7321.667  8494.167
10023.333  9450.000
[298] 10995.833 10166.667 10881.667  8832.500 10015.000 11526.667 11015.833
10857.500  9627.500
[307] 11716.667 10213.333  9765.000  8673.333  8560.833 10153.333 12676.667
11633.333 10933.333
[316] 10492.500 10300.000  9853.333 10602.500  9778.333  9030.000 12785.833
10950.000 11523.333
[325] 12445.000 10896.667 10875.833 11619.167 13154.167 11693.333 12561.667
11002.500 11017.500
[334] 10700.833 14557.500 12236.667 10875.833 11762.500 12705.000 13452.500
11402.500 11232.500
[343] 11739.167 13043.333 11583.333 11468.333 11772.500 13278.333 14787.500
13462.500 13837.500
[352] 13491.667 12455.000 14412.500 14211.667 15135.000 13923.333 15175.000
16048.333 16182.500
[361] 17939.167 17191.667 18522.500 18923.333 17106.667 15255.833 14752.500
15868.333 21636.667
[370] 17807.500 19451.667 17064.167 20163.333 16959.167 17077.500 19470.833
17424.167 18996.667
[379] 24262.500 21454.167 21115.833 18455.000 22215.000 27894.167 28049.167
30302.500 37730.000
> y<-media
> tempo<-c(-193:193/193)
> tempo
  [1] -1.000000000 -0.994818653 -0.989637306 -0.984455959 -0.979274611
-0.974093264 -0.968911917
  [8] -0.963730570 -0.958549223 -0.953367876 -0.948186528 -0.943005181
-0.937823834 -0.932642487
 [15] -0.927461140 -0.922279793 -0.917098446 -0.911917098 -0.906735751
-0.901554404 -0.896373057
 [22] -0.891191710 -0.886010363 -0.880829016 -0.875647668 -0.870466321
-0.865284974 -0.860103627
 [29] -0.854922280 -0.849740933 -0.844559585 -0.839378238 -0.834196891
-0.829015544 -0.823834197
 [36] -0.818652850 -0.813471503 -0.808290155 -0.803108808 -0.797927461
-0.792746114 -0.787564767
 [43] -0.782383420 -0.777202073 -0.772020725 -0.766839378 -0.761658031
-0.756476684 -0.751295337
 [50] -0.746113990 -0.740932642 -0.735751295 -0.730569948 -0.725388601
-0.720207254 -0.715025907
 [57] -0.709844560 -0.704663212 -0.699481865 -0.694300518 -0.689119171
-0.683937824 -0.678756477
 [64] -0.673575130 -0.668393782 -0.663212435 -0.658031088 -0.652849741
-0.647668394 -0.642487047
 [71] -0.637305699 -0.632124352 -0.626943005 -0.621761658 -0.616580311
-0.611398964 -0.606217617
 [78] -0.601036269 -0.595854922 -0.590673575 -0.585492228 -0.580310881
-0.575129534 -0.569948187
 [85] -0.564766839 -0.559585492 -0.554404145 -0.549222798 -0.544041451
-0.538860104 -0.533678756
 [92] -0.528497409 -0.523316062 -0.518134715 -0.512953368 -0.507772021
-0.502590674 -0.497409326
 [99] -0.492227979 -0.487046632 -0.481865285 -0.476683938 -0.471502591
-0.466321244 -0.461139896
[106] -0.455958549 -0.450777202 -0.445595855 -0.440414508 -0.435233161
-0.430051813 -0.424870466
[113] -0.419689119 -0.414507772 -0.409326425 -0.404145078 -0.398963731
-0.393782383 -0.388601036
[120] -0.383419689 -0.378238342 -0.373056995 -0.367875648 -0.362694301
-0.357512953 -0.352331606
[127] -0.347150259 -0.341968912 -0.336787565 -0.331606218 -0.326424870
-0.321243523 -0.316062176
[134] -0.310880829 -0.305699482 -0.300518135 -0.295336788 -0.290155440
-0.284974093 -0.279792746
[141] -0.274611399 -0.269430052 -0.264248705 -0.259067358 -0.253886010
-0.248704663 -0.243523316
[148] -0.238341969 -0.233160622 -0.227979275 -0.222797927 -0.217616580
-0.212435233 -0.207253886
[155] -0.202072539 -0.196891192 -0.191709845 -0.186528497 -0.181347150
-0.176165803 -0.170984456
[162] -0.165803109 -0.160621762 -0.155440415 -0.150259067 -0.145077720
-0.139896373 -0.134715026
[169] -0.129533679 -0.124352332 -0.119170984 -0.113989637 -0.108808290
-0.103626943 -0.098445596
[176] -0.093264249 -0.088082902 -0.082901554 -0.077720207 -0.072538860
-0.067357513 -0.062176166
[183] -0.056994819 -0.051813472 -0.046632124 -0.041450777 -0.036269430
-0.031088083 -0.025906736
[190] -0.020725389 -0.015544041 -0.010362694 -0.005181347  0.000000000
 0.005181347  0.010362694
[197]  0.015544041  0.020725389  0.025906736  0.031088083  0.036269430
 0.041450777  0.046632124
[204]  0.051813472  0.056994819  0.062176166  0.067357513  0.072538860
 0.077720207  0.082901554
[211]  0.088082902  0.093264249  0.098445596  0.103626943  0.108808290
 0.113989637  0.119170984
[218]  0.124352332  0.129533679  0.134715026  0.139896373  0.145077720
 0.150259067  0.155440415
[225]  0.160621762  0.165803109  0.170984456  0.176165803  0.181347150
 0.186528497  0.191709845
[232]  0.196891192  0.202072539  0.207253886  0.212435233  0.217616580
 0.222797927  0.227979275
[239]  0.233160622  0.238341969  0.243523316  0.248704663  0.253886010
 0.259067358  0.264248705
[246]  0.269430052  0.274611399  0.279792746  0.284974093  0.290155440
 0.295336788  0.300518135
[253]  0.305699482  0.310880829  0.316062176  0.321243523  0.326424870
 0.331606218  0.336787565
[260]  0.341968912  0.347150259  0.352331606  0.357512953  0.362694301
 0.367875648  0.373056995
[267]  0.378238342  0.383419689  0.388601036  0.393782383  0.398963731
 0.404145078  0.409326425
[274]  0.414507772  0.419689119  0.424870466  0.430051813  0.435233161
 0.440414508  0.445595855
[281]  0.450777202  0.455958549  0.461139896  0.466321244  0.471502591
 0.476683938  0.481865285
[288]  0.487046632  0.492227979  0.497409326  0.502590674  0.507772021
 0.512953368  0.518134715
[295]  0.523316062  0.528497409  0.533678756  0.538860104  0.544041451
 0.549222798  0.554404145
[302]  0.559585492  0.564766839  0.569948187  0.575129534  0.580310881
 0.585492228  0.590673575
[309]  0.595854922  0.601036269  0.606217617  0.611398964  0.616580311
 0.621761658  0.626943005
[316]  0.632124352  0.637305699  0.642487047  0.647668394  0.652849741
 0.658031088  0.663212435
[323]  0.668393782  0.673575130  0.678756477  0.683937824  0.689119171
 0.694300518  0.699481865
[330]  0.704663212  0.709844560  0.715025907  0.720207254  0.725388601
 0.730569948  0.735751295
[337]  0.740932642  0.746113990  0.751295337  0.756476684  0.761658031
 0.766839378  0.772020725
[344]  0.777202073  0.782383420  0.787564767  0.792746114  0.797927461
 0.803108808  0.808290155
[351]  0.813471503  0.818652850  0.823834197  0.829015544  0.834196891
 0.839378238  0.844559585
[358]  0.849740933  0.854922280  0.860103627  0.865284974  0.870466321
 0.875647668  0.880829016
[365]  0.886010363  0.891191710  0.896373057  0.901554404  0.906735751
 0.911917098  0.917098446
[372]  0.922279793  0.927461140  0.932642487  0.937823834  0.943005181
 0.948186528  0.953367876
[379]  0.958549223  0.963730570  0.968911917  0.974093264  0.979274611
 0.984455959  0.989637306
[386]  0.994818653  1.000000000
> x<-tempo
>
> dados <- data.frame(x=x,y=y)
> dados2 <- dados[-c(1,387),]
>
> xyplot(y~x, data=dados, type=c("p","smooth"))
>
> ## Valores iniciais.
>
> guess <- list(c=1.0,a=1.0, b=0.001)
>
> ## Modelo não linear.
> func <- function(x, c,a,b){
+   y <- (atanh((sqrt((x+(b*(x^2)))^2)/c)^2) + a)
+   return(y)
+ }
> with(guess,
+      curve(func(x, c,a,b),min(dados$x), max(dados$x)))
Mensagens de aviso perdidas:
In atanh((sqrt((x + (b * (x^2)))^2)/c)^2) : NaNs produzidos
> abline(v=194, lty=2)
>
>
> ## Sobrepondo dados e função a partir dos valores iniciais.
> plot(y~x, data=dados, xlab="tempo", ylab="volume/média")
>
>
> f <- expression(atanh((sqrt((x+(b*(x^2)))^2)/c)^2) + a)
>
> fit <- nls(y~func(x, c,a,b), data=dados2, start=guess, trace=TRUE)
55389072390 :  1.000 1.000 0.001
6020970037 :   -224.49929 10765.82603    49.04659
Erro em nls(y ~ func(x, c, a, b), data = dados2, start = guess, trace =
TRUE) :
  fator de passos 0.000488281 reduzido abaixo de 'minFactor' de 0.000976562
>

Muito Obrigada,

Michelle