Program MARK
- Program MARK Summary
- Advances since 1985
- Encounter Techniques
- Types of Encounter History Data
- Estimable Parameters
- Models in MARK
- Additional Features
- Knowledge Required of the User
- Data are Required to Gain Reliable Knowledge
- Computer Requirements
- Program Architecture
- Vocabulary of MARK
- Basic Data Slide
- Likelihood Function Slide
- Pr(Enc. History)
- Dead Encounters
- Seber 1970
- Brownie et al. 1985
- Dead Encounters Slide
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Dead Encounters
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Live Encounters (CJS)
- Extensions to CJS
- Multi-Strata Model
- Jolly-Seber Model
- Robust Design
- Robust-design Multi-strata Models
- Joint Encounters
- Joint Encounters
- Joint Encounters
- Joint Encounters
- Joint Encounters
- Barker Live-Dead Model
- Robust Design Barker Model
- Robust Design Barker Model
- Multi-strata with Live and Dead Encounters
- Joint Encounters: Estimation of Radio Effects
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures
- Closed Captures with Heterogeneity
- Closed Captures with Heterogeneity
- Closed Captures
- Closed Captures
- Known Fate Slide
- Known Fate
- Known Fate
- Known Fate
- Known Fate
- Known Fate
- Known Fate Equivalent to Kaplan Meier
- Advantages of Using MARK Known Fate over Kaplan-Meier
- Model Building
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- Parameter Index Matrix � PIM
- PIM Manipulation
- PIM Chart
- Design Matrix
- Individual Covariates
- PIMs for Design Matrix Examples
- Model {theta(g*t)}
- Link Functions
- Design Matrix
- Design Matrix
- Design Matrix
- Logit vs. Real Parameters
- Model {theta(.)}
- Model {theta(g)}
- Model {theta(t)}
- Model {theta(g + t)}
- Model {theta(g*t)}
- Model {theta(T)}
- Model {theta(g + T)}
- Model {theta(g*T)}
- Design Matrix Manipulation
- Design Matrix Functions
- Numerical Estimation
- Numerical Methods
- Features of Results BrowseR
- Hypothesis Tests
- Goodness-of-fit Procedures
- Advanced Analyses
- Model Selection
- Model Selection Information-Theoretic Approach
- Model Selection
- Selecting �Best� Model
- Model Averaging
- Model Averaging
- Model Averaging
- Variance Components
- Process Variance �2
- Variance Components
- Variance Components
- Variance Components
- Example
- Example Continued
- Program MARK Assumptions
- Quasi-likelihood Estimation
- Goodness of Fit
- Goodness of Fit � Logistic Regression
- Goodness-of-fit Testing
- Bootstrap Procedure
- Goodness of Fit � Bootstrap Approach
- Logistic Regression Procedure
- Logistic Regression Procedure
- Logistic Regression Procedure
- Program Documentation
- Program Availability
Program MARK
Slide 1 of 139
Advances since 1985
Slide 2 of 139
Encounter Techniques
Slide 3 of 139
Types of Encounter History Data
Slide 4 of 139
Estimable Parameters
Slide 5 of 139
Models in MARK
Slide 6 of 139
Additional Features
Slide 7 of 139
Knowledge Required of the User
Slide 8 of 139
Data are Required to Gain Reliable Knowledge
Slide 9 of 139
Computer Requirements
Slide 10 of 139
Program Architecture
Slide 11 of 139
Vocabulary of MARK
Slide 12 of 139
Basic Data
Slide 13 of 139
Likelihood Function
Slide 14 of 139
Pr(Enc. History)
Slide 15 of 139
Dead Encounters
Slide 16 of 139
Seber 1970
Slide 17 of 139
Brownie et al. 1985 Model
Slide 18 of 139
Dead Encounters
Slide 19 of 139
Dead Encounters
Slide 20 of 139
Dead Encounters
Slide 21 of 139
Dead Encounters
Slide 22 of 139
Dead Encounters
Slide 23 of 139
Dead Encounters
Slide 24 of 139
Dead Encounters
Slide 25 of 139
Dead Encounters
Slide 26 of 139
Dead Encounters
Slide 27 of 139
Dead Encounters
Slide 28 of 139
Dead Encounters
Slide 29 of 139
Live Encounters (CJS)
Slide 30 of 139
Live Encounters (CJS)
Slide 31 of 139
Live Encounters (CJS)
Slide 32 of 139
Live Encounters (CJS)
Slide 33 of 139
Live Encounters (CJS)
Slide 34 of 139
Live Encounters (CJS)
Slide 35 of 139
Live Encounters (CJS)
Slide 36 of 139
Live Encounters (CJS)
Slide 37 of 139
Live Encounters (CJS)
Slide 38 of 139
Live Encounters (CJS)
Slide 39 of 139
Live Encounters (CJS)
Slide 40 of 139
Extensions to CJS
Slide 41 of 139
Multi-Strata Model
Slide 42 of 139
Jolly-Seber Model
Slide 43 of 139
Robust Design
Slide 44 of 139
Robust-design Multi-strata Models
Slide 45 of 139
Joint Encounters
Slide 46 of 139
Joint Encounters
Slide 47 of 139
Joint Encounters
Slide 48 of 139
Joint Encounters
Slide 49 of 139
Joint Encounters
Slide 50 of 139
Barker Live-Dead Model
Slide 51 of 139
Robust Design Barker Model
Slide 52 of 139
Robust Design Barker Model
Slide 53 of 139
Multi-strata with Live and Dead Encounters
Slide 54 of 139
Joint Encounters: Estimation of Radio Effects
Slide 55 of 139
Closed Captures
Slide 56 of 139
Closed Captures
Slide 57 of 139
Closed Captures
Slide 58 of 139
Closed Captures
Slide 59 of 139
Closed Captures
Slide 60 of 139
Closed Captures
Slide 61 of 139
Closed Captures
Slide 62 of 139
Closed Captures
Slide 63 of 139
Closed Captures
Slide 64 of 139
Closed Captures with Heterogeneity
Slide 65 of 139
Closed Captures with Heterogeneity
Slide 66 of 139
Closed Captures
Slide 67 of 139
Closed Captures
Slide 68 of 139
Known Fate
Slide 69 of 139
Known Fate
Slide 70 of 139
Known Fate
Slide 71 of 139
Known Fate
Slide 72 of 139
Known Fate
Slide 73 of 139
Known Fate
Slide 74 of 139
Known Fate Equivalent to Kaplan Meier
Slide 75 of 139
Advantages of Using MARK Known Fate over Kaplan-Meier
Slide 76 of 139
Model Building
Slide 77 of 139
Parameter Index Matrix � PIM
Slide 78 of 139
Parameter Index Matrix � PIM
Slide 79 of 139
Parameter Index Matrix � PIM
Slide 80 of 139
Parameter Index Matrix � PIM
Slide 81 of 139
Parameter Index Matrix � PIM
Slide 82 of 139
Parameter Index Matrix � PIM
Slide 83 of 139
Parameter Index Matrix � PIM
Slide 84 of 139
Parameter Index Matrix � PIM
Slide 85 of 139
PIM Manipulation
Slide 86 of 139
PIM Chart
Slide 87 of 139
Design Matrix
Slide 88 of 139
Individual Covariates
Slide 89 of 139
PIMs for Design Matrix Examples
Slide 90 of 139
Model {theta(g*t)}
Slide 91 of 139
Link Functions
Slide 92 of 139
Design Matrix
Slide 93 of 139
Design Matrix
Slide 94 of 139
Design Matrix
Slide 95 of 139
Logit vs. Real Parameters
Slide 96 of 139
Model {theta(.)}
Slide 97 of 139
Model {theta(g)}
Slide 98 of 139
Model {theta(t)}
Slide 99 of 139
Model {theta(g + t)}
Slide 100 of 139
Model {theta(g*t)}
Slide 101 of 139
Model {theta(T)}
Slide 102 of 139
Model {theta(g + T)}
Slide 103 of 139
Model {theta(g*T)}
Slide 104 of 139
Design Matrix Manipulation
Slide 105 of 139
Design Matrix Functions
Slide 106 of 139
Numerical Estimation
Slide 107 of 139
Numerical Methods
Slide 108 of 139
Features of Results Browser
Slide 109 of 139
Hypothesis Tests
Slide 110 of 139
Goodness-of-fit Procedures
Slide 111 of 139
Advanced Analyses
Slide 112 of 139
Model Selection
Slide 113 of 139
Model Selection Information-Theoretic Approach
Slide 114 of 139
Model Selection
Slide 115 of 139
Selecting �Best� Model
Slide 116 of 139
Model Averaging
Slide 117 of 139
Model Averaging
Slide 119 of 139
Model Averaging
Slide 120 of 139
Variance Components
Slide 121 of 139
Process Variance �2
Slide 122 of 139
Variance Components
Slide 123 of 139
Variance Components
Slide 124 of 139
Variance Components
Slide 125 of 139
Example
Slide 126 of 139
Example Continued
Slide 127 of 139
Program MARK Assumptions
Slide 128 of 139
Quasi-likelihood Estimation
Slide 129 of 139
Goodness of Fit
Slide 130 of 139
Goodness of Fit � Logistic Regression
Slide 131 of 139
Goodness-of-fit Testing
Slide 132 of 139
Bootstrap Procedure
Slide 133 of 139
Goodness of Fit � Bootstrap Approach
Slide 134 of 139
Logistic Regression Procedure
Slide 135 of 139
Logistic Regression Procedure
Slide 136 of 139
Logistic Regression Procedure
Slide 137 of 139
Program Documentation
Slide 138 of 139
Program Availability
Slide 139 of 139
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Program MARK Summary
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Last update: 5/19/2017