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`c1 c2 c3 c4`

[1,] 1 0 0 0

[2,] 1 0 0 0

[3,] 1 1 0 0

[4,] 1 1 0 0

[5,] 1 0 0 0

The top of the year matrix looks like this:

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`c1 c2 c3 c4`

1 2021 2021 2021 2021

2 2021 2021 2021 2021

3 2021 2021 2021 2021

4 2021 2021 2021 2021

5 2020 2020 2020 2020

The top of the season matrix looks like this:

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`c1 c2 c3 c4`

1 1 1 1 1

2 1 1 1 1

3 2 2 2 2

4 2 2 2 2

5 2 2 2 2

Both year and season are integers. Here is the code for both models:

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`year.cjs <- F.cjs.estim(capture = ~ 1, survival = ~ year.mat,`

histories=ch.mat)

seas.cjs <- F.cjs.estim(capture = ~ 1, survival = ~ seas.mat,

histories=ch.mat)

Here are the results:

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`F.cjs.estim(capture = ~1, survival = ~year.mat, histories = ch.mat)`

Capture var Est SE Survival var Est SE

(Intercept) 2.04405 0.64901 (Intercept) 1.889 0.49182

year.mat -0.00111 0.00024

Message = SUCCESS: Convergence criterion met

Link = logit

Model df = 1

Std Errors and QAIC adjusted for C_hat = 1 on 0 df

Log likelihood = -97.5681262896564

Deviance = 195.136252579313

AIC = 197.136252579313

AICc = 197.185635295362

QAIC = 197.136252579313

QAICc = 197.185635295362

Population Size Estimates (se):

N2=40 (3.71), N3=11 (1.47), N4=7 (1.06),

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`F.cjs.estim(capture = ~1, survival = ~seas.mat, histories = ch.mat)`

Capture var Est SE Survival var Est SE

(Intercept) 1.82267 0.61578 (Intercept) 0.44488 0.56501

seas.mat -0.33433 0.25645

Message = SUCCESS: Convergence criterion met

Link = logit

Model df = 3

Std Errors and QAIC adjusted for C_hat = 1 on 0 df

Log likelihood = -96.5329746777274

Deviance = 193.065949355455

AIC = 199.065949355455

AICc = 199.369746823809

QAIC = 199.065949355455

QAICc = 199.369746823809

Population Size Estimates (se):

N2=41 (4.32), N3=12 (1.69), N4=7 (1.22),

Why does the df differ between models?