- Code: Select all
* * WARNING * * Invalid calculation occurred during processing derived parameters of this model.
IEEE flag status at end of processing derived parameters:
overflow F
divide by zero F
invalid T
underflow F
inexact T
I have investigated this warning with deterministic fake data (provided below). Both MARK and RMark return the correct parameter estimates and the correct derived seasonal occupancies. I am not sure whether the lambdas are correct because I keep forgetting exactly what they represent. I am not really interested in the lambdas.
My inclination is to ignore this warning, but I wanted to ask advice from others here first. I also provide my RMark code below so anyone can reproduce this warning with my fake data.
A little background that led to this issue. Initially I did not stratify my sites by habitat. However, gradually I came to realize a subset of sites with a particular habitat appeared to be unoccupied. The species of interest was never detected in any of the sites with this habitat. So, I started thinking I should create at least one model with occupancy = 0 for sites in the perceived poor-quality habitat. This would allow me to get a better idea of the occupancy dynamics in the other sites. And I could perhaps use AIC to support constraining occupancy = 0 in the perceived poor-quality sites. I could not do the latter if I just deleted the perceived poor-quality sites from the data set.
Thank you for any thoughts on ignoring or resolving the warning message with either MARK or RMark.
Here is my RMark code, my fake data set and the estimates returned by RMark and MARK:
- Code: Select all
library(RMark)
n.days <- 4
mydata <- convert.inp("fake_msocc_data_Apr20_2023.inp",
group.df = data.frame(habitat=c("good", "poor")))
head(mydata)
# Process data
mydata.processed = process.data(mydata, begin.time = 1, model = "RDOccupEG",
time.intervals = c(rep(c(0,1), (n.days-1)),0),
groups=("habitat"))
# Create default design data
mydata.ddl = make.design.data(mydata.processed)
# examine data
head(mydata.ddl$Psi)
head(mydata.ddl$Epsilon)
head(mydata.ddl$Gamma)
head(mydata.ddl$p)
# occupancy probability
psihabitat = list(formula = ~ -1 + habitat)
# detection probability
pdayhabitat = list(formula = ~ -1 + session : habitat)
# epsilon probability
ehabitat = list(formula = ~ -1 + habitat)
# gamma probability
ghabitat = list(formula = ~ -1 + habitat)
mydata.ddl$Psi$fix[mydata.ddl$Psi$habitat == 'poor'] <- 0
mydata.ddl$Epsilon$fix[mydata.ddl$Epsilon$habitat == 'poor'] <- 1
mydata.ddl$Gamma$fix[mydata.ddl$Gamma$habitat == 'poor'] <- 0
mydata.ddl$p$fix[mydata.ddl$p$habitat == 'poor'] <- 0
habitat.model = mark(mydata.processed, mydata.ddl,
model.parameters = list( Psi = psihabitat,
p = pdayhabitat,
Epsilon = ehabitat,
Gamma = ghabitat),
output=FALSE, silent=TRUE)
habitat.model
# LOGIT Link Function Parameters of { Psi(~-1 + habitat)Epsilon(~-1 + habitat)Gamma(~-1 + habitat)p(~-1 + session:habitat) }
# 95% Confidence Interval
# Parameter Beta Standard Error Lower Upper
# ------------------------- -------------- -------------- -------------- --------------
# 1:Psi:habitatgood -1.3862943 0.0791937 -1.5415139 -1.2310747
# 2:Epsilon:habitatgood 1.4000000 0.0828573 1.2375998 1.5624003
# 3:Gamma:habitatgood -0.1500000 0.0461333 -0.2404212 -0.0595787
# 4:p:session1:habitatgo 2.9444390 0.2411869 2.4717127 3.4171654
# 5:p:session2:habitatgo 2.1972244 0.1286285 1.9451126 2.4493363
# 6:p:session3:habitatgo 1.7346009 0.1219025 1.4956719 1.9735299
# 7:p:session4:habitatgo 1.3862944 0.1114515 1.1678495 1.6047393
# * * WARNING * * Invalid calculation occurred during processing derived parameters of this model.
# IEEE flag status at end of processing derived parameters:
# overflow F
# divide by zero F
# invalid T
# underflow F
# inexact T
# from Program MARK
#
# LOGIT Link Function Parameters of {psi(hab) epsilon(hab) gamma(hab) p(t, hab) C}
# 95% Confidence Interval
# Parameter Beta Standard Error Lower Upper
# ------------------------- -------------- -------------- -------------- --------------
# 1: -1.3862944 0.0791937 -1.5415140 -1.2310748
# 2: 0.0000000 0.0000000 0.0000000 0.0000000
# 3: 1.4000000 0.0828573 1.2375997 1.5624002
# 4: 0.0000000 0.0000000 0.0000000 0.0000000
# 5: -0.1500000 0.0461333 -0.2404212 -0.0595788
# 6: 0.0000000 0.0000000 0.0000000 0.0000000
# 7: 2.9444389 0.2411869 2.4717127 3.4171652
# 8: 0.0000000 0.0000000 0.0000000 0.0000000
# 9: 2.1972246 0.1286285 1.9451127 2.4493365
# 10: 1.7346010 0.1219025 1.4956720 1.9735300
# 11: 1.3862943 0.1114515 1.1678494 1.6047392
Here is my fake data set:
- Code: Select all
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00000001 17.479379385232 0 ;
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00000000 0 1000 ;