Olá,
você diz eu colar aqui o dput ou mandar em anexo o data-set?

Em 8 de outubro de 2015 23:04, sznelwar <sznelwar@uol.com.br> escreveu:

> Tem o data-set para rodar?
>
>
> ------------------------------
>
>
> 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
> > dados2
> >
> > xyplot(y~x, data=dados, type=c("p","smooth"))
> >
> > ## Valores iniciais.
> >
> > guess
> >
> > ## Modelo não linear.
> > func
> +   y
> +   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
> 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
>
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