PIM Chart Slide 87 of 139

Go to slide: 1: Program MARK 2: Advances since 1985 3: Encounter Techniques 4: Types of Encounter History Data 5: Estimable Parameters 6: Models in MARK 7: Additional Features 8: Knowledge Required of the User 9: Data are Required to Gain Reliable Knowledge 10: Computer Requirements 11: Program Architecture 12: Vocabulary of MARK 13: Basic Data 14: Likelihood Function 15: Pr(Enc. History) 16: Dead Encounters 17: Seber 1970 18: Brownie et al. 1985 Model 19: Dead Encounters 20: Dead Encounters 21: Dead Encounters 22: Dead Encounters 23: Dead Encounters 24: Dead Encounters 25: Dead Encounters 26: Dead Encounters 27: Dead Encounters 28: Dead Encounters 29: Dead Encounters 30: Live Encounters (CJS) 31: Live Encounters (CJS) 32: Live Encounters (CJS) 33: Live Encounters (CJS) 34: Live Encounters (CJS) 35: Live Encounters (CJS) 36: Live Encounters (CJS) 37: Live Encounters (CJS) 38: Live Encounters (CJS) 39: Live Encounters (CJS) 40: Live Encounters (CJS) 41: Extensions to CJS 42: Multi-Strata Model 43: Jolly-Seber Model 44: Robust Design 45: Robust-design Multi-strata Models 46: Joint Encounters 47: Joint Encounters 48: Joint Encounters 49: Joint Encounters 50: Joint Encounters 51: Barker Live-Dead Model 52: Robust Design Barker Model 53: Robust Design Barker Model 54: Multi-strata with Live and Dead Encounters 55: Joint Encounters: Estimation of Radio Effects 56: Closed Captures 57: Closed Captures 58: Closed Captures 59: Closed Captures 60: Closed Captures 61: Closed Captures 62: Closed Captures 63: Closed Captures 64: Closed Captures 65: Closed Captures with Heterogeneity 66: Closed Captures with Heterogeneity 67: Closed Captures 68: Closed Captures 69: Known Fate 70: Known Fate 71: Known Fate 72: Known Fate 73: Known Fate 74: Known Fate 75: Known Fate Equivalent to Kaplan Meier 76: Advantages of Using MARK Known Fate over Kaplan-Meier 77: Model Building 78: Parameter Index Matrix – PIM 79: Parameter Index Matrix – PIM 80: Parameter Index Matrix – PIM 81: Parameter Index Matrix – PIM 82: Parameter Index Matrix – PIM 83: Parameter Index Matrix – PIM 84: Parameter Index Matrix – PIM 85: Parameter Index Matrix – PIM 86: PIM Manipulation 87: PIM Chart 88: Design Matrix 89: Individual Covariates 90: PIMs for Design Matrix Examples 91: Model {theta(g*t)} 92: Link Functions 93: Design Matrix 94: Design Matrix 95: Design Matrix 96: Logit vs. Real Parameters 97: Model {theta(.)} 98: Model {theta(g)} 99: Model {theta(t)} 100: Model {theta(g + t)} 101: Model {theta(g*t)} 102: Model {theta(T)} 103: Model {theta(g + T)} 104: Model {theta(g*T)} 105: Design Matrix Manipulation 106: Design Matrix Functions 107: Numerical Estimation 108: Numerical Methods 109: Features of Results Browser 110: Hypothesis Tests 111: Goodness-of-fit Procedures 112: Advanced Analyses 113: Model Selection 114: Model Selection Information-Theoretic Approach 115: Model Selection 116: Selecting “Best” Model 117: Model Averaging 118: Model Averaging 119: Model Averaging 120: Model Averaging 121: Variance Components 122: Process Variance €2 123: Variance Components 124: Variance Components 125: Variance Components 126: Example 127: Example Continued 128: Program MARK Assumptions 129: Quasi-likelihood Estimation 130: Goodness of Fit 131: Goodness of Fit – Logistic Regression 132: Goodness-of-fit Testing 133: Bootstrap Procedure 134: Goodness of Fit – Bootstrap Approach 135: Logistic Regression Procedure 136: Logistic Regression Procedure 137: Logistic Regression Procedure 138: Program Documentation 139: Program Availability