CODEWITHSUNDEEP
rnls
Hark
Hark

Reputation: 115

R nls Different Errors occur

I'm new in R programming and I don't get a solution to an error which occurs when I use the nls Function. I try to fit the data from an ecdf (values are extracted and saved in y) to this function model with four parameters:

fitsim <- nls(y ~ exp(-(((a-Abfluss)/(c*(Abfluss-b)))^d)), 
           start = list( a=max(Abfluss), b=min(Abfluss), 
                         c=3, d=1))

When I start the nls Function these error occurs:

 Error in numericDeriv(form[[3L]], names(ind), env) : 
  Fehlender Wert oder etwas Unendliches durch das Modell erzeugt

which means there is a missing value ore some value with infinity is generated through the model. My vectors Abfluss and y have both the same lengths. Aim is to get the parameter estimation. Maybe the problem is, that the model only works under this conditions: c>0, d>0, b<=Abfluss<=a. I try already the na.rm=True command. Then another error appears:

 Error in model.frame.default(formula = ~y + Abfluss, na.rm = TRUE) : 
  Variablenlängen sind unterschiedlich (gefunden für '(na.rm)')

which means, the Length of variables are different.

I appreciative for every kind of help and advice.

For a better understanding I attach my whole code with whole data:

time<-c(1851:2013)
Abfluss<-    c(4853,4214,5803,3430,4645,4485,3100,4797,4030,3590,5396,9864,3683,4485,4064,3420,5396,
        4895,3931,4238,3790,3520,4263,5474,3790,4700,5109,4525,4007,6340,4993,6903,8160,3600,3480,3540,
        3540,4565,3333,7764,  
        4755,7940,3112,3169,4435,5365,9422,3150,10500,4512,3790,4618,6126,3769,3704,
         5938,5669,4552,5458,5854,4867,6057,4783,5753,5736,4618,6091,5820,5007,7984, 4435,
         4645,7465,5820,5988,6022,4300,6062,3302,4877,4586,5275,4410,3174,4966,4939,4638,
         5541,5760,6495,5435,4952,4912,6092,5182,5820,5129,6436,6648,3063,5550,5160,4400,
         9600,6400,6380,6300,6180,6899,4360,5550,4580,3894,5277,7520,6780,5100,5430,4550,
         6620,4050,4560,5290,6610,8560,4943,6940,4744,6650,5700,7440,6200,4597,3697,7300,
         4644,5456,6302,3741,5398,9500,6296,5279,5923,6412,6559,6559,5891,5737,5010,5790,
         10300,4150,4870,6740,7560,8010,5120,8170,7430, 7330,5900, 11150)


#EV4-Distribution
dEV4 <- function(x, a, b, c,d) {
m<-exp(-(((a-Abfluss)/(c*(Abfluss-b)))^d))
return(m)
}

#Simulation example
Sim<-dEV4(Abfluss,a=max(Abfluss),b=min(Abfluss), c=3, d=1)
dEV4cdf<-cbind(Abfluss,Sim)

#Empirical cdf
p = ecdf(Abfluss)  
y<- p(Abfluss) #Extracting of cumulated probabilities
m<-cbind(Abfluss,y)

#plot EV4 and ecdf
plot(dEV4cdf, type="p",main="EV4")
plot(ecdf(Abfluss), add=T)

#Fitting EV4 nls
fitsim <- nls(y ~ exp(-(((a-Abfluss)/(c*(Abfluss-b)))^d)), 
          start = list( a=max(Abfluss), b=min(Abfluss), 
                        c=3, d=1), na.rm=TRUE)

Upvotes: 0

Views: 1907

Answers (1)

G. Grothendieck
G. Grothendieck

Reputation: 269481

Do not use starting values that are on the boundary of the feasible region and try nlxb in nlmrt instead (which can be used with the same arguments except data = is not optional):

library(nlmrt)
fitsim <- nlxb(y ~ exp(-(((a - Abfluss) / (c * (Abfluss - b))) ^ d)), 
          data = data.frame(y, Abfluss),
          start = list(a = 2 * max(Abfluss), b = min(Abfluss) / 2, c = 3, d = 1))

plot(y ~ Abfluss, pch = 20)
o <- order(Abfluss)
fit <- y - fitsim$resid
lines(fit[o] ~ Abfluss[o], col = "red")

giving:

nlmrt class object: x 
residual sumsquares =  0.02908  on  163 observations
    after  5001    Jacobian and  6060 function evaluations
  name            coeff          SE       tstat      pval      gradient    JSingval   
a                20047.7            NA         NA         NA   1.119e-07        3251  
b               -1175384            NA         NA         NA   1.432e-09      0.1775  
c              0.0129414            NA         NA         NA     -0.1296   5.808e-06  
d                 12.146            NA         NA         NA  -2.097e-06   6.798e-11

screenshot

Upvotes: 2

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