'Wrong range component' error message from TMB (Maximum gradient component did not calculated)

Question

Dear all TMB users with warm heart or users who use C++ in R about to help me.

As an infant TMB user, I have questions about my error messages from TMB.

I found that there are errors in my code. (The 2nd error is revised)

The results are from gdbsource()

1: Incomplete final line found on my cpp file (Actually this is a warning message, not the error. I'd like to know why this happened.)

2: Following error message emerging after compilation:

Error in ev(obj$env$par): Wrong range component.

Error in ev(obj$env$par) : Wrong range component.
check1:            1
0.208386
9.70444e-005
6.95024e-005
8.33432e-005
7.90787e-005
5.96018e-005
7.0229e-005
9.99291e-005
0.000112216
0.000101964
9.13024e-005
8.54733e-005
8.77122e-005
8.77124e-005
8.36922e-005
6.80879e-005
0.000135715
0.00014771
6.97471e-005
5.73876e-005
5.2996e-005
7.63701e-005
check2: 0.367879
check3: 20.0855
check4: 0.00273944
check5: 0.0450492
check6: 0.0301974
Optimizing tape... Done
Error in ev(obj$env$par) : Wrong range component.
In addition: Warning messages:
  1: In nlminb(model$par, model$fn, model$gr) : NA/NaN function evaluation
2: In he(par) : restarting interrupted promise evaluation
outer mgc:  NaN
Error in nlminb(model$par, model$fn, model$gr) :
  gradient function must return a numeric vector of length 5
Execution halted
[Inferior 1 (process 8152) exited with code 01]
C:\Users\POPDYN~1\AppData\Local\Temp\RtmpkL2d5A\file1f1048dc1743:4: Error in sourced command file:
  No stack.
(gdb)

Do you have any idea about the second error?

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I appreciate your effort, feedback, your tips, and your help in advance!

I attached my cpp, R codes, and data.

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I would like to post more contents on this page. The main problem in my code thought to be defining parameter or vector.

Following the comment, I ran my code included 'cout' part. and the result

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CPP code (revised)

template<class Type>
Type objective_function<Type>::operator() ()
{
  //data
  DATA_VECTOR(C);
  DATA_VECTOR(I);
  int n = C.size();

  //free parameters
  PARAMETER(logR);
  PARAMETER(logK);
  PARAMETER(logQ);
  PARAMETER(logsdproc);   //log(sd) in the process error;
  PARAMETER(logSigma);
  PARAMETER_VECTOR(P);

  Type r = exp(logR);
  Type k = exp(logK);
  Type q = exp(logQ);
  Type sdproc = exp(logsdproc); 
  Type sigma = exp(logSigma);

  //derived parameters
   vector<Type> Ihat(n);

  Type f = 0.0;
  Type fpen = 0.0;
  Type tmpP;
  P(0)=1.0;

  for(int t=0; t<(n-1); t++)   {

    //P(t)=B(t)/k;
    tmpP = P(t) + r*P(t)*(1-P(t))-C(t)/k;
    P(t+1) = posfun(tmpP, Type(0.01), fpen);

    f += fpen;
    f -= dnorm(log(P(t+1)), log(tmpP), sdproc, true);
    };

  for(int t=0; t<n; t++)   {
    Ihat(t)=q*P(t)*k;
  };

  f -= sum(dnorm(log(I), log(Ihat), sigma, true));

  REPORT(P);
  REPORT(Ihat);     // plot
  REPORT(fpen);

  std::cout << " check1: " << P << std::endl; //thank you Wave!
  std::cout << " check2: " << r << std::endl;
  std::cout << " check3: " << k << std::endl;
  std::cout << " check4: " << q << std::endl;
  std::cout << " check5: " << sdproc << std::endl;
  std::cout << " check6: " << sigma << std::endl;
  return f;
}

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albacore <- read.table("albacore.csv", header=TRUE, sep=",")
albacore
names(albacore) <- c("t", "C", "I")
n=c(dim(albacore)[1])  #the number of Bs

parameters <- list(logR=-1.0, logK=3.0, logQ=-5.9, logsdproc=-3.1, logSigma=-3.5, P=rep(0.3,n));
parameters

require(TMB)

compile("scalbav2.cpp", "-O1 -g", DLLFLAGS="")
dyn.load(dynlib("scalbav2"))

library(TMB)
gdbsource("scalbav2.R", interactive=TRUE)

################################################################################

model<- MakeADFun(albacore, parameters, random="P", DLL="scalbav2")

model$par

length(parameters$P)

fit <- nlminb(model$par, model$fn, model$gr)
rep <- sdreport(model)

print(summary(rep))

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Data

year    catch   cpue
1967    15.9    61.89
1968    25.7    78.98
1969    28.5    55.59
1970    23.7    44.61
1971    25.0    56.89
1972    33.3    38.27
1973    28.2    33.84
1974    19.7    36.13
1975    17.5    41.95
1976    19.3    36.63
1977    21.6    36.33
1978    23.1    38.82
1979    22.5    34.32
1980    22.5    37.64
1981    23.6    34.01
1982    29.1    32.16
1983    14.4    26.88
1984    13.2    36.61
1985    28.4    30.07
1986    34.6    30.75
1987    37.5    23.36
1988    25.9    22.36
1989    25.3    21.91

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I also attached sessioninfo:

R version 3.5.0 (2018-04-23)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows >= 8 (build 9200)
attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] TMB_1.7.13

loaded via a namespace (and not attached):
[1] compiler_3.5.0  Matrix_1.2-14   tools_3.5.0     grid_3.5.0     
[5] lattice_0.20-35