Problems with the new version 2.1.9

posts related to the RMark library, which may not be of general interest to users of 'classic' MARK

Problems with the new version 2.1.9

Postby sergi_20 » Thu Nov 06, 2014 9:50 am

Hi all,

A few months ago, I did run my data on RMark to work on the robust design analysis.

I tried to run it again today with the new version 2.1.9, but it is giving me a problem to get the output.

Using default formula for c

S.dot.GammaDoublePrime.dot.GammaPrime.dot.p.time.session.N.session
STOP ERROR
Error in extract.mark.output(out, model, adjust, realvcv, vcvfile) :
MARK did not run properly. If error message was not shown, re-run MARK with invisible=FALSE

Any thoughts?

Code: Select all

#convert inp file
rd.data=convert.inp("C:/R/RD/Qi 2-3 (5th fortnight-all days)_without calves and sightings(identified dolphins).inp",use.comments=T)

#Process data specifying primary and secondary capture occasions
time.intervals=c(0,0,0,0,0,3,0,0,3,0,3,0,0,0,3,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,12,0,0,0,3,0,0,0,0,0,0,3,0,0,3,0,0,6,0,0,0,0,0)
rd.process=process.data(rd.data,begin.time=1,model="Robust",time.intervals=time.intervals)

#Create the design data
rd.ddl=make.design.data(rd.process)

#add covariates
rd.ddl$p$season[rd.ddl$p$session==1]=1
rd.ddl$p$season[rd.ddl$p$session==4]=2
rd.ddl$p$season[rd.ddl$p$session==7]=3
rd.ddl$p$season[rd.ddl$p$session==10]=4
rd.ddl$p$season[rd.ddl$p$session==13]=1
rd.ddl$p$season[rd.ddl$p$session==16]=2
rd.ddl$p$season[rd.ddl$p$session==19]=3
rd.ddl$p$season[rd.ddl$p$session==31]=3
rd.ddl$p$season[rd.ddl$p$session==34]=4
rd.ddl$p$season[rd.ddl$p$session==37]=1
rd.ddl$p$season[rd.ddl$p$session==40]=2
rd.ddl$p$season[rd.ddl$p$session==46]=4

rd.ddl$c$season[rd.ddl$c$session==1]=1
rd.ddl$c$season[rd.ddl$c$session==4]=2
rd.ddl$c$season[rd.ddl$c$session==7]=3
rd.ddl$c$season[rd.ddl$c$session==10]=4
rd.ddl$c$season[rd.ddl$c$session==13]=1
rd.ddl$c$season[rd.ddl$c$session==16]=2
rd.ddl$c$season[rd.ddl$c$session==19]=3
rd.ddl$c$season[rd.ddl$c$session==31]=3
rd.ddl$c$season[rd.ddl$c$session==34]=4
rd.ddl$c$season[rd.ddl$c$session==37]=1
rd.ddl$c$season[rd.ddl$c$session==40]=2
rd.ddl$c$season[rd.ddl$c$session==46]=4

rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==1]=1
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==4]=2
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==7]=3
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==10]=4
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==13]=1
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==16]=2
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==19]=3
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==31]=3
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==34]=4
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==37]=1
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==40]=2
rd.ddl$GammaDoublePrime$season[rd.ddl$GammaDoublePrime$time==46]=4

rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==1]=1
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==4]=2
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==7]=3
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==10]=4
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==13]=1
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==16]=2
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==19]=3
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==31]=3
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==34]=4
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==37]=1
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==40]=2
rd.ddl$GammaPrime$season[rd.ddl$GammaPrime$time==46]=4

rd.ddl$S$season[rd.ddl$S$time==1]=1
rd.ddl$S$season[rd.ddl$S$time==4]=2
rd.ddl$S$season[rd.ddl$S$time==7]=3
rd.ddl$S$season[rd.ddl$S$time==10]=4
rd.ddl$S$season[rd.ddl$S$time==13]=1
rd.ddl$S$season[rd.ddl$S$time==16]=2
rd.ddl$S$season[rd.ddl$S$time==19]=3
rd.ddl$S$season[rd.ddl$S$time==31]=3
rd.ddl$S$season[rd.ddl$S$time==34]=4
rd.ddl$S$season[rd.ddl$S$time==37]=1
rd.ddl$S$season[rd.ddl$S$time==40]=2
rd.ddl$S$season[rd.ddl$S$time==46]=4

rd.ddl$p$Year[rd.ddl$p$session==1]=1
rd.ddl$p$Year[rd.ddl$p$session==4]=1
rd.ddl$p$Year[rd.ddl$p$session==7]=1
rd.ddl$p$Year[rd.ddl$p$session==10]=1
rd.ddl$p$Year[rd.ddl$p$session==13]=2
rd.ddl$p$Year[rd.ddl$p$session==16]=2
rd.ddl$p$Year[rd.ddl$p$session==19]=2
rd.ddl$p$Year[rd.ddl$p$session==31]=3
rd.ddl$p$Year[rd.ddl$p$session==34]=3
rd.ddl$p$Year[rd.ddl$p$session==37]=4
rd.ddl$p$Year[rd.ddl$p$session==40]=4
rd.ddl$p$Year[rd.ddl$p$session==46]=4

rd.ddl$c$Year[rd.ddl$c$session==1]=1
rd.ddl$c$Year[rd.ddl$c$session==4]=1
rd.ddl$c$Year[rd.ddl$c$session==7]=1
rd.ddl$c$Year[rd.ddl$c$session==10]=1
rd.ddl$c$Year[rd.ddl$c$session==13]=2
rd.ddl$c$Year[rd.ddl$c$session==16]=2
rd.ddl$c$Year[rd.ddl$c$session==19]=2
rd.ddl$c$Year[rd.ddl$c$session==31]=3
rd.ddl$c$Year[rd.ddl$c$session==34]=3
rd.ddl$c$Year[rd.ddl$c$session==37]=4
rd.ddl$c$Year[rd.ddl$c$session==40]=4
rd.ddl$c$Year[rd.ddl$c$session==46]=4

rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==1]=1
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==4]=1
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==7]=1
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==10]=1
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==13]=2
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==16]=2
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==19]=2
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==31]=3
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==34]=3
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==37]=4
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==40]=4
rd.ddl$GammaDoublePrime$Year[rd.ddl$GammaDoublePrime$time==46]=4

rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==1]=1
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==4]=1
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==7]=1
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==10]=1
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==13]=2
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==16]=2
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==19]=2
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==31]=3
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==34]=3
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==37]=4
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==40]=4
rd.ddl$GammaPrime$Year[rd.ddl$GammaPrime$time==46]=4

rd.ddl$S$Year[rd.ddl$S$time==1]=1
rd.ddl$S$Year[rd.ddl$S$time==4]=1
rd.ddl$S$Year[rd.ddl$S$time==7]=1
rd.ddl$S$Year[rd.ddl$S$time==10]=1
rd.ddl$S$Year[rd.ddl$S$time==13]=2
rd.ddl$S$Year[rd.ddl$S$time==16]=2
rd.ddl$S$Year[rd.ddl$S$time==19]=2
rd.ddl$S$Year[rd.ddl$S$time==31]=3
rd.ddl$S$Year[rd.ddl$S$time==34]=3
rd.ddl$S$Year[rd.ddl$S$time==37]=4
rd.ddl$S$Year[rd.ddl$S$time==40]=4
rd.ddl$S$Year[rd.ddl$S$time==46]=4

#View design matrix
rd.ddl

#Markovian emigration
rd.markovian.models=function()
{
  S.dot=list(formula=~1)
  S.season=list(formula=~season)
  S.year=list(formula=~Year)
  S.time=list(formula=~time)
  p.time.session=list(formula=~-1+session:time,share=TRUE)
  GammaDoublePrime.season=list(formula=~season)
  GammaDoublePrime.year=list(formula=~Year)
  GammaDoublePrime.dot=list(formula=~1)
  GammaDoublePrime.time=list(formula=~time)
  GammaPrime.season=list(formula=~season)
  GammaPrime.year=list(formula=~Year)
  GammaPrime.dot=list(formula=~1)
  GammaPrime.time=list(formula=~time)
  N.session=list(formula=~session)
  cml=create.model.list("Robust")
  results=mark.wrapper(cml,data=rd.process,ddl=rd.ddl,adjust=FALSE)
  return(results) 
}
rd.markovian.results=rd.markovian.models()



Many thanks,
Sergi
sergi_20
 
Posts: 16
Joined: Wed Apr 03, 2013 1:01 pm

Re: Problems with the new version 2.1.9

Postby jlaake » Thu Nov 06, 2014 10:30 am

Are you using the most recent version of MARK? The parameter N was changed to f0 in MARK. If you are using an older version of MARK then it will fail. It should have shown you the output file. If it did, look and see what errors were shown.

--jeff
jlaake
 
Posts: 1480
Joined: Fri May 12, 2006 12:50 pm
Location: Escondido, CA

Re: Problems with the new version 2.1.9

Postby sergi_20 » Thu Nov 06, 2014 11:28 am

I downloaded MARK version 8.0 and the RMark version 2.1.9

This is the output:

Program MARK - Survival Rate Estimation with Capture-Recapture Data
gfortran(Win32) Vers. 8.0 Jun 2014 6-Nov-2014 15:24:48 Page 001
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

This version was compiled by GCC version 4.8.1 using the options
-m32 -mtune=generic -march=x86-64 -mthreads -O2 -fimplicit-none -fbounds-check -funroll-loops -ftree-vectorize -fopenmp.
This problem will use 3 of 4 possible threads.


INPUT --- proc title ;

CPU Time in seconds for last procedure was 0.00


INPUT --- proc chmatrix occasions= 54 groups= 1 etype= Robust ICMeans
INPUT --- NoHist hist= 75 ;

INPUT --- time interval 0 0 0 0 0 3 0 0 3 0 3 0 0 0 3 0 0 0 0 0 3 0 0
INPUT --- 0 0 3 0 0 0 0 12 0 0 0 3 0 0 0 0 0 0 3 0 0 3 0 0 6 0 0 0 0
INPUT --- 0 ;

INPUT --- glabel(1)=Group 1;

Number of unique encounter histories read was 75.

Number of individual covariates read was 0.
Time interval lengths vary and/or not equal to 1.

Data type number is 7
Data type is Robust Design with Full Likelihhood p and c

CPU Time in seconds for last procedure was 0.02

Program MARK - Survival Rate Estimation with Capture-Recapture Data
gfortran(Win32) Vers. 8.0 Jun 2014 6-Nov-2014 15:24:48 Page 002
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

INPUT --- proc estimate link=Parm-Specific NOLOOP varest=2ndPart ;


INPUT --- model={ S(~1)Gamma''(~1)Gamma'(~1)p(~-1 +
INPUT --- session:time)c()N(~session) };

INPUT --- group=1 S rows=11 cols=11 Triang ;
INPUT --- 1 1 1 1 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 1 ;
INPUT --- 1 1 1 1 1 ;
INPUT --- 1 1 1 1 ;
INPUT --- 1 1 1 ;
INPUT --- 1 1 ;
INPUT --- 1 ;

INPUT --- group=1 Gamma'' rows=11 cols=11 Triang ;
INPUT --- 2 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 2 ;
INPUT --- 2 2 2 2 2 ;
INPUT --- 2 2 2 2 ;
INPUT --- 2 2 2 ;
INPUT --- 2 2 ;
INPUT --- 2 ;

INPUT --- group=1 Gamma' rows=10 cols=10 Triang ;
INPUT --- 3 3 3 3 3 3 3 3 3 3 ;
INPUT --- 3 3 3 3 3 3 3 3 3 ;
INPUT --- 3 3 3 3 3 3 3 3 ;
INPUT --- 3 3 3 3 3 3 3 ;
INPUT --- 3 3 3 3 3 3 ;
INPUT --- 3 3 3 3 3 ;
INPUT --- 3 3 3 3 ;
INPUT --- 3 3 3 ;
INPUT --- 3 3 ;
INPUT --- 3 ;


Program MARK - Survival Rate Estimation with Capture-Recapture Data
gfortran(Win32) Vers. 8.0 Jun 2014 6-Nov-2014 15:24:48 Page 003
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

INPUT --- group=1 p Session 1 rows=1 cols=6 Square ;
INPUT --- 4 5 6 7 8 9 ;

INPUT --- group=1 p Session 2 rows=1 cols=3 Square ;
INPUT --- 10 11 12 ;

INPUT --- group=1 p Session 3 rows=1 cols=2 Square ;
INPUT --- 13 14 ;

INPUT --- group=1 p Session 4 rows=1 cols=4 Square ;
INPUT --- 15 16 17 18 ;

INPUT --- group=1 p Session 5 rows=1 cols=6 Square ;
INPUT --- 19 20 21 22 23 24 ;

INPUT --- group=1 p Session 6 rows=1 cols=5 Square ;
INPUT --- 25 26 27 28 29 ;

INPUT --- group=1 p Session 7 rows=1 cols=5 Square ;
INPUT --- 30 31 32 33 34 ;

INPUT --- group=1 p Session 8 rows=1 cols=4 Square ;
INPUT --- 35 36 37 38 ;

INPUT --- group=1 p Session 9 rows=1 cols=7 Square ;
INPUT --- 39 40 41 42 43 44 45 ;

INPUT --- group=1 p Session 10 rows=1 cols=3 Square ;
INPUT --- 46 47 48 ;

INPUT --- group=1 p Session 11 rows=1 cols=3 Square ;
INPUT --- 49 50 51 ;

INPUT --- group=1 p Session 12 rows=1 cols=6 Square ;
INPUT --- 52 53 54 55 56 57 ;

INPUT --- group=1 c Session 1 rows=1 cols=5 Square ;
INPUT --- 5 6 7 8 9 ;

INPUT --- group=1 c Session 2 rows=1 cols=2 Square ;
INPUT --- 11 12 ;

INPUT --- group=1 c Session 3 rows=1 cols=1 Square ;
INPUT --- 14 ;

INPUT --- group=1 c Session 4 rows=1 cols=3 Square ;

Program MARK - Survival Rate Estimation with Capture-Recapture Data
gfortran(Win32) Vers. 8.0 Jun 2014 6-Nov-2014 15:24:48 Page 004
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

INPUT --- 16 17 18 ;

INPUT --- group=1 c Session 5 rows=1 cols=5 Square ;
INPUT --- 20 21 22 23 24 ;

INPUT --- group=1 c Session 6 rows=1 cols=4 Square ;
INPUT --- 26 27 28 29 ;

INPUT --- group=1 c Session 7 rows=1 cols=4 Square ;
INPUT --- 31 32 33 34 ;

INPUT --- group=1 c Session 8 rows=1 cols=3 Square ;
INPUT --- 36 37 38 ;

INPUT --- group=1 c Session 9 rows=1 cols=6 Square ;
INPUT --- 40 41 42 43 44 45 ;

INPUT --- group=1 c Session 10 rows=1 cols=2 Square ;
INPUT --- 47 48 ;

INPUT --- group=1 c Session 11 rows=1 cols=2 Square ;
INPUT --- 50 51 ;

INPUT --- group=1 c Session 12 rows=1 cols=5 Square ;
INPUT --- 53 54 55 56 57 ;
DATTYP = 7
group = 1 N Session 1 rows = 1 cols = 1 Square

Parameter specified was not one of the recognized parameters:
a', a'', alpha, c, d, delta, epsilon, epsilonA, epsilonAB, epsilonB, epsilonBA,
f, F, F', f0, gamma, gamma', gamma'', gammaA, gammaAB, gammaB, gammaBA, lambda,
M, N, Nbar, Nstar, omega, p, p1, p2, pA, pB, pent, phi, phi0, pi,
psi, psi1, psi2, psiA, psiAB, psiB, psiBA, psiBa, ptilde, R, R', r, rA,
rAB, rAb, raB, rBA, rBa, S, sigma, sigmaf, sigmap, sigmaphi, sigmar, sigmaS,
theta, theta', U, or VarAdd


INPUT --- group=1 N Session 1 rows=1 cols=1 Square ;

ERROR -- Type of Parameter Input Matrix (PIM) was not specified correctly.


sergi_20
 
Posts: 16
Joined: Wed Apr 03, 2013 1:01 pm

Re: Problems with the new version 2.1.9

Postby sergi_20 » Thu Nov 06, 2014 11:30 am

This is one of the 63 outputs
sergi_20
 
Posts: 16
Joined: Wed Apr 03, 2013 1:01 pm

Re: Problems with the new version 2.1.9

Postby jlaake » Thu Nov 06, 2014 11:35 am

Please try and let me know what happens

example(robust)

it should give summaries with f0 instead of N. You may need to remove the code that specifies N formula. They should be changed to f0 - f and then zero.

--jeff
jlaake
 
Posts: 1480
Joined: Fri May 12, 2006 12:50 pm
Location: Escondido, CA

Re: Problems with the new version 2.1.9

Postby sergi_20 » Thu Nov 06, 2014 12:05 pm

This is what I get from example (robust):

robust> ## No test:
robust> data(robust)

robust> run.robust=function()
robust+ {
robust+ #
robust+ # data from Robust.dbf with MARK
robust+ # 5 primary sessions with secondary sessions of length 2,2,4,5,2
robust+ #
robust+ time.intervals=c(0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
robust+ #
robust+ # Random emigration, p=c varies by time and session, S by time
robust+ #
robust+ S.time=list(formula=~time)
robust+ p.time.session=list(formula=~-1+session:time,share=TRUE)
robust+ GammaDoublePrime.random=list(formula=~time,share=TRUE)
robust+ model.1=mark(data = robust, model = "Robust",
robust+ time.intervals=time.intervals,
robust+ model.parameters=list(S=S.time,
robust+ GammaDoublePrime=GammaDoublePrime.random,p=p.time.session),threads=2)
robust+ #
robust+ # Random emigration, p varies by session, uses Mh but pi fixed to 1,
robust+ # S by time.This model is in the example Robust with MARK but it is
robust+ # a silly example because it uses the heterogeneity model but then fixes
robust+ # pi=1 which means there is no heterogeneity.Probably the data were
robust+ # not generated under Mh. See results of model.2.b
robust+ #
robust+ pi.fixed=list(formula=~1,fixed=1)
robust+ p.session=list(formula=~-1+session,share=TRUE)
robust+ model.2.a=mark(data = robust, model = "RDHet",
robust+ time.intervals=time.intervals,
robust+ model.parameters=list(S=S.time,
robust+ GammaDoublePrime=GammaDoublePrime.random,
robust+ p=p.session,pi=pi.fixed),threads=2)
robust+ #
robust+ # Random emigration, p varies by session, uses Mh and in this
robust+ # case pi varies and so does p across
robust+ # mixtures with an additive session effect.
robust+ #
robust+ pi.dot=list(formula=~1)
robust+ p.session.mixture=list(formula=~session+mixture,share=TRUE)
robust+ model.2.b=mark(data = robust, model = "RDHet",
robust+ time.intervals=time.intervals,
robust+ model.parameters=list(S=S.time,
robust+ GammaDoublePrime=GammaDoublePrime.random,
robust+ p=p.session.mixture,pi=pi.dot),threads=2)
robust+ #
robust+ # Markov constant emigration rates, pi varies by session,
robust+ # p=c varies by session, S constant
robust+ # This model is in the example Robust with MARK
robust+ # but it is a silly example because it
robust+ # uses the heterogeneity model but then fixes pi=1
robust+ # which means there is no heterogeneity.
robust+ # Probably the data were not generated under Mh.
robust+ # See results of model.3.b
robust+ #
robust+ S.dot=list(formula=~1)
robust+ pi.session=list(formula=~session)
robust+ p.session=list(formula=~-1+session,share=TRUE)
robust+ GammaDoublePrime.dot=list(formula=~1)
robust+ GammaPrime.dot=list(formula=~1)
robust+ model.3.a=mark(data = robust, model = "RDHet",
robust+ time.intervals=time.intervals,
robust+ model.parameters=list(S=S.dot,
robust+ GammaPrime=GammaPrime.dot,
robust+ GammaDoublePrime=GammaDoublePrime.dot,
robust+ p=p.session,pi=pi.session),threads=2)
robust+ #
robust+ # Markov constant emigration rates, pi varies by session,
robust+ # p=c varies by session+mixture, S constant. This is model.3.a
robust+ # but allows pi into the model by varying p/c by mixture.
robust+ #
robust+ S.dot=list(formula=~1)
robust+ pi.session=list(formula=~session)
robust+ GammaDoublePrime.dot=list(formula=~1)
robust+ GammaPrime.dot=list(formula=~1)
robust+ model.3.b=mark(data = robust, model = "RDHet",
robust+ time.intervals=time.intervals,
robust+ model.parameters=list(S=S.dot,
robust+ GammaPrime=GammaPrime.dot,
robust+ GammaDoublePrime=GammaDoublePrime.dot,
robust+ p=p.session.mixture,pi=pi.session),threads=2)
robust+ #
robust+ # Huggins Random emigration, p=c varies by time and session,
robust+ # S by time
robust+ # Beware that this model is not quite the same
robust+ # as the others above that say random emigration because
robust+ # the rates have been fixed for the last 2 occasions.
robust+ # That was done with PIMS in the MARK example and
robust+ # here it is done by binning the times so that times 3 and 4
robust+ # are in the same bin, so the time model
robust+ # has 3 levels (1,2, and 3-4). By doing so the parameters
robust+ # become identifiable but this may not be
robust+ # reasonable depending on the particulars of the data.
robust+ # Note that the same time binning must be done both for
robust+ # GammaPrime and GammaDoublePrime because the parameters are
robust+ # the same in the random emigration model. If you
robust+ # forget to bin one of the parameters across time it will fit
robust+ # a model but it won't be what you expect as it will
robust+ # not share parameters. Note the use of the argument "right".
robust+ # This controls whether binning is inclusive on the right (right=TRUE)
robust+ # or on the left (right=FALSE). Using "right" nested in the list
robust+ # of design parameters is equivalent to using it as a calling
robust+ # argument to make.design.data or add.design.data.
robust+ #
robust+ S.time=list(formula=~time)
robust+ p.time.session=list(formula=~-1+session:time,share=TRUE)
robust+ GammaDoublePrime.random=list(formula=~time,share=TRUE)
robust+ model.4=mark(data = robust, model = "RDHuggins",
robust+ time.intervals=time.intervals,design.parameters=
robust+ list(GammaDoublePrime=list(time.bins=c(1,2,5))),
robust+ right=FALSE, model.parameters=
robust+ list(S=S.time,GammaDoublePrime=GammaDoublePrime.random,
robust+ p=p.time.session),threads=2)
robust+
robust+ return(collect.models())
robust+ }

robust> robust.results=run.robust()
STOP ERROR
Error in extract.mark.output(out, model, adjust, realvcv, vcvfile) :
MARK did not run properly. If error message was not shown, re-run MARK with invisible=FALSE


********Following model failed to run : S(~time)Gamma''(~time)Gamma'()p(~-1 + session:time)c()N(~session) **********

STOP ERROR
Error in extract.mark.output(out, model, adjust, realvcv, vcvfile) :
MARK did not run properly. If error message was not shown, re-run MARK with invisible=FALSE


********Following model failed to run : S(~time)Gamma''(~time)Gamma'()pi(~1)p(~-1 + session)N(~session) **********

STOP ERROR
Error in extract.mark.output(out, model, adjust, realvcv, vcvfile) :
MARK did not run properly. If error message was not shown, re-run MARK with invisible=FALSE


********Following model failed to run : S(~time)Gamma''(~time)Gamma'()pi(~1)p(~session + mixture)N(~session) **********

STOP ERROR
Error in extract.mark.output(out, model, adjust, realvcv, vcvfile) :
MARK did not run properly. If error message was not shown, re-run MARK with invisible=FALSE


********Following model failed to run : S(~1)Gamma''(~1)Gamma'(~1)pi(~session)p(~-1 + session)N(~session) **********

STOP ERROR
Error in extract.mark.output(out, model, adjust, realvcv, vcvfile) :
MARK did not run properly. If error message was not shown, re-run MARK with invisible=FALSE


********Following model failed to run : S(~1)Gamma''(~1)Gamma'(~1)pi(~session)p(~session + mixture)N(~session) **********

STOP NORMAL EXIT

Note: only 23 parameters counted of 24 specified parameters
AICc and parameter count have been adjusted upward

Output summary for RDHuggins model
Name : S(~time)Gamma''(~time)Gamma'()p(~-1 + session:time)c()

Npar : 24 (unadjusted=23)
-2lnL: 15171.74
AICc : 15219.95 (unadjusted=15217.931)

Beta
estimate se lcl ucl
S:(Intercept) 2.0526287 0.1319554 1.7939960 2.3112613
S:time2 -0.1438988 0.2137030 -0.5627567 0.2749591
S:time3 -0.9578834 0.1669904 -1.2851847 -0.6305822
S:time4 -0.8197548 0.3108348 -1.4289909 -0.2105187
GammaDoublePrime:(Intercept) -2.3357439 0.3198024 -2.9625566 -1.7089311
GammaDoublePrime:time[2,5] 0.3976205 0.3417112 -0.2721336 1.0673745
GammaDoublePrime:time2 0.5913244 0.7867673 -0.9507395 2.1333882
GammaDoublePrime:time3 -0.0667471 0.7451787 -1.5272974 1.3938033
GammaDoublePrime:time4 -9.8184248 196.7323000 -395.4137500 375.7769000
p:session1:time1 0.7799540 0.0845527 0.6142308 0.9456773
p:session2:time1 0.4993871 0.0955532 0.3121027 0.6866714
p:session3:time1 -0.0484523 0.0773395 -0.2000377 0.1031330
p:session4:time1 0.3114560 0.0904455 0.1341828 0.4887292
p:session5:time1 -0.0534904 0.1463075 -0.3402532 0.2332723
p:session1:time2 0.5968164 0.0795540 0.4408906 0.7527423
p:session2:time2 0.2808001 0.0895252 0.1053306 0.4562695
p:session3:time2 0.8808612 0.0863940 0.7115290 1.0501934
p:session4:time2 -0.0062495 0.0892004 -0.1810823 0.1685833
p:session5:time2 -0.1335406 0.1435421 -0.4148832 0.1478020
p:session3:time3 0.4930188 0.0803210 0.3355895 0.6504480
p:session4:time3 0.7979536 0.0969625 0.6079072 0.9880001
p:session3:time4 0.4252890 0.0796102 0.2692530 0.5813250
p:session4:time4 0.7704909 0.0964394 0.5814697 0.9595121
p:session4:time5 0.3600897 0.0908417 0.1820401 0.5381394


Real Parameter S

1 2 3 4
1 0.886213 0.8708764 0.7492742 0.7743212
2 0.8708764 0.7492742 0.7743212
3 0.7492742 0.7743212
4 0.7743212


Real Parameter GammaDoublePrime

1 2 3 4
1 0.0882056 0.1258542 0.1258542 0.1258542
2 0.1258542 0.1258542 0.1258542
3 0.1258542 0.1258542
4 0.1258542


Real Parameter GammaPrime

2 3 4
1 0.1487524 0.0829829 5.266345e-06
2 0.0829829 5.266345e-06
3 5.266345e-06


Real Parameter p
Session:1
1 2
0.6856702 0.6449276

Session:2
1 2
0.6223153 0.5697424

Session:3
1 2 3 4
0.4878893 0.7070007 0.6208173 0.6047482

Session:4
1 2 3 4 5
0.5772406 0.4984376 0.6895366 0.6836271 0.5890622

Session:5
1 2
0.4866306 0.4666644


Real Parameter c
Session:1
2
0.6449276

Session:2
2
0.5697424

Session:3
2 3 4
0.7070007 0.6208173 0.6047482

Session:4
2 3 4 5
0.4984376 0.6895366 0.6836271 0.5890622

Session:5
2
0.4666644
Model 1: model.1 has not been run.
Model 2: model.2.a has not been run.
Model 3: model.2.b has not been run.
Model 4: model.3.a has not been run.
Model 5: model.3.b has not been run.
Error: object 'savetype' not found



And I can't find the f0.
Should I delete the "N.session=list(formula=~session)" from the formula and re-run it again?
Where I should include the f0?

Sergi
sergi_20
 
Posts: 16
Joined: Wed Apr 03, 2013 1:01 pm

Re: Problems with the new version 2.1.9

Postby jlaake » Thu Nov 06, 2014 1:21 pm

I sent an email message to your account. Check there for suggestions. Let's figure this out offlist and then we'll post final solution.
jlaake
 
Posts: 1480
Joined: Fri May 12, 2006 12:50 pm
Location: Escondido, CA

Re: Problems with the new version 2.1.9

Postby jlaake » Thu Nov 06, 2014 3:27 pm

Sergi and I solved this offlist. He was using R2.1.5. There was no binary for RMark 2.1.9 for this old version of R. After he upgraded R and reinstalled RMark, it all ran fine.

It is important to look at what is shown when you type library(RMark). It will reply with the RMark version number that you are using.

--jeff
jlaake
 
Posts: 1480
Joined: Fri May 12, 2006 12:50 pm
Location: Escondido, CA


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