by Alisha Mosloff » Tue Sep 23, 2025 5:05 pm
Hello,
I am running in to a similar issue.
I have mark-recapture data from 5 sites trapped over 4 years with 8-11 trap nights per session. Not every site was trapped in every year. I also have 1 habitat mask which includes covariate data. When I run even simple models, all parameter estimates are NA. My code is below. Any insight is appreciated.
coonCH=read.capthist(captfile="RaccoonCaptureHistory.csv", trapfile=
list("Cole2021_Raccoons_TrapFile.csv", "Cole2022_Raccoons_TrapFile.csv",
"Cole2024_Raccoons_TrapFile.csv", "Hinkle2021_Raccoons_TrapFile.csv",
"Hinkle2022_Raccoons_TrapFile.csv", "Hinkle2023_Raccoons_TrapFile.csv",
"Hinkle2024_Raccoons_TrapFile.csv", "RebelsCove2021_Raccoons_TrapFile.csv",
"RebelsCove2022_Raccoons_TrapFile.csv", "RebelsCove2023_Raccoons_TrapFile.csv",
"RebelsCove2024_Raccoons_TrapFile.csv", "Roesline2023_Raccoons_TrapFile.csv",
"LakeThunderhead2022_Raccoons_TrapFile.csv",
"LakeThunderhead2023_Raccoons_TrapFile.csv", "LakeThunderhead2024_Raccoons_TrapFile.csv"), detector="single",
# noccasions=c(10, 10, 8, 11),
skip=1, covnames=c('Age', 'Sex', 'Date', 'Temp', 'Management', 'SunsetLocal', 'SunriseLocal', 'MoonriseLocal',
'MoonsetLocal', 'MeanMoonlightIntensity', 'nightstart', 'nightend', 'nightduration',
'moonstart', 'moonend', 'moonduration', 'proportion', 'propnocloud', 'MRI'))
coonCH=shareFactorLevels(coonCH)
summary(coonCH, terse=TRUE)
# Cole21 Cole22 Cole24 Hinkle21 Hinkle22 Hinkle23 Hinkle24 Rebels21 Rebels22 Rebels23 Rebels24
# Occasions 10 11 10 10 11 10 10 10 7 10 11
# Detections 23 17 18 29 9 38 17 33 18 40 24
# Animals 12 15 16 16 9 22 16 23 15 26 15
# Detectors 41 41 41 43 43 43 41 38 38 38 38
# Roesline23 Thunderhead22 Thunderhead23 Thunderhead24
# Occasions 8 11 9 8
# Detections 36 23 1 2
# Animals 20 9 1 2
# Detectors 46 37 37 37
mask_points=read.csv("RaccoonHabitatMaskData.csv", sep=",")
my_mask=read.mask(data=mask_points, spacing=100)
covariates(my_mask)
#All covariates plot as expected
MRI <- secr.fit(
coonCH,
model = list(
D ~ predicted_counts, # Density depends on Management
g0 ~ MRI, # Detection depends on covariates
sigma ~ 1
),
mask = my_mask,
detectfn = "HN", # e.g., Half-normal detection function
CL=TRUE
)
MRI
secr.fit(capthist = coonCH, model = list(D ~ predicted_counts,
g0 ~ MRI, sigma ~ 1), mask = my_mask, CL = TRUE, detectfn = "HN")
secr 5.2.4, 16:01:25 23 Sep 2025
$Cole21
Detector type single
Detector number 41
Average spacing 93.05912 m
x-range 471908 472906 m
y-range 4486651 4487356 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 1 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Cole22
Detector type single
Detector number 41
Average spacing 93.05912 m
x-range 471908 472906 m
y-range 4486651 4487356 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10 11
min 0 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1 1
$Cole24
Detector type single
Detector number 41
Average spacing 93.05912 m
x-range 471908 472906 m
y-range 4486651 4487356 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Hinkle21
Detector type single
Detector number 43
Average spacing 100 m
x-range 509752 510462 m
y-range 4480629 4481757 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Hinkle22
Detector type single
Detector number 43
Average spacing 100 m
x-range 509752 510462 m
y-range 4480629 4481757 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10 11
min 0 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1 1
$Hinkle23
Detector type single
Detector number 43
Average spacing 100 m
x-range 509752 510462 m
y-range 4480629 4481757 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Hinkle24
Detector type single
Detector number 41
Average spacing 100 m
x-range 509752 510462 m
y-range 4480629 4481647 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Rebels21
Detector type single
Detector number 38
Average spacing 99.22405 m
x-range 524452.9 525354.1 m
y-range 482918.6 484620 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Rebels22
Detector type single
Detector number 38
Average spacing 99.19116 m
x-range 525254 526952 m
y-range 4489765 4490671 m
Usage range by occasion
1 2 3 4 5 6 7
min 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1
$Rebels23
Detector type single
Detector number 38
Average spacing 99.19116 m
x-range 525254 526952 m
y-range 4489765 4490671 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10
min 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1
$Rebels24
Detector type single
Detector number 38
Average spacing 99.19116 m
x-range 525254 526952 m
y-range 4489765 4490671 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10 11
min 0 0 0 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1 1
$Roesline23
Detector type single
Detector number 46
Average spacing 75.88004 m
x-range 507302.6 507760 m
y-range 4477152 4477759 m
Usage range by occasion
1 2 3 4 5 6 7 8
min 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1
$Thunderhead22
Detector type single
Detector number 37
Average spacing 94.02127 m
x-range 497152 497873 m
y-range 4485956 4486656 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10 11
min 0 0 0 1 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1 1 1 1
$Thunderhead23
Detector type single
Detector number 37
Average spacing 94.02127 m
x-range 497152 497873 m
y-range 4485956 4486656 m
Usage range by occasion
1 2 3 4 5 6 7 8 9
min 0 0 0 1 0 0 0 1 0
max 1 1 1 1 1 1 1 1 1
$Thunderhead24
Detector type single
Detector number 37
Average spacing 94.02127 m
x-range 497152 497873 m
y-range 4485956 4486656 m
Usage range by occasion
1 2 3 4 5 6 7 8
min 0 0 0 0 0 0 0 0
max 1 1 1 1 1 1 1 1
Cole21 Cole22 Cole24 Hinkle21 Hinkle22 Hinkle23 Hinkle24 Rebels21 Rebels22 Rebels23
Occasions 10 11 10 10 11 10 10 10 7 10
Detections 23 17 18 29 9 38 17 33 18 40
Animals 12 15 16 16 9 22 16 23 15 26
Detectors 41 41 41 43 43 43 41 38 38 38
Rebels24 Roesline23 Thunderhead22 Thunderhead23 Thunderhead24
Occasions 11 8 11 9 8
Detections 24 36 23 1 2
Animals 15 20 9 1 2
Detectors 38 46 37 37 37
Model : D~predicted_counts g0~MRI sigma~1
Fixed (real) : none
Detection fn : halfnormal
N parameters : 4
Log likelihood : -1e+10
AIC : 2e+10
AICc : 2e+10
Beta parameters (coefficients)
beta SE.beta lcl ucl
D.predicted_counts NA NA NA NA
g0 NA NA NA NA
g0.MRI NA NA NA NA
sigma NA NA NA NA
Variance-covariance matrix of beta parameters
NULL
Fitted (real) parameters evaluated at base levels of covariates
Error in beta.vcv[is.na(fb[row(beta.vcv)]) & is.na(fb[col(beta.vcv)])] <- object$beta.vcv :
replacement has length zero