I am conducting a robust design study to estimate the abundance, temporary emigration and survival of a dolphin population. I have identified some transients in my population through the TEST 3. I want to correct the survival estimates incorporating transients into the model. For this, I have added in the design matrix a column to differentiate between resident and transient, following a similar approach to the Time since marking from Chapter 7 "Age and cohort models from MARK book.
- Code: Select all
#Load Rmark package
library (RMark)
#set working directory
setwd("C:/R/RD")
#convert inp file
rd.data=convert.inp("C:/R/RD/61_occasions.inp",use.comments=T)
#Process data specifying primary and secondary capture occasions
time.intervals=c(0,0,0,0,0,3,0,0,3,0,3,0,0,0,3,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,3,0,3,0,3,0,3,0,0,0,3,0,0,0,0,0,3,0,0,3,0,0,3,0,3,0,0,0,0,0)
rd.process=process.data(rd.data,begin.time=1,model="Robust",time.intervals=time.intervals)
#Create the design data
rd.ddl=make.design.data(rd.process)
rd.ddl=add.design.data(rd.process,rd.ddl,parameter="S",type="age",
bins=c(0,1,42),name="transients", replace=TRUE)
##This is the output
rd.ddl$S
par.index model.index group cohort age time occ.cohort Cohort Age Time transients
1 1 1 1 1 0 1 1 0 0 0 [0,1]
2 2 2 1 1 3 4 1 0 3 3 (1,42]
3 3 3 1 1 6 7 1 0 6 6 (1,42]
4 4 4 1 1 9 10 1 0 9 9 (1,42]
5 5 5 1 1 12 13 1 0 12 12 (1,42]
6 6 6 1 1 15 16 1 0 15 15 (1,42]
7 7 7 1 1 18 19 1 0 18 18 (1,42]
8 8 8 1 1 21 22 1 0 21 21 (1,42]
9 9 9 1 1 24 25 1 0 24 24 (1,42]
10 10 10 1 1 27 28 1 0 27 27 (1,42]
11 11 11 1 1 30 31 1 0 30 30 (1,42]
12 12 12 1 1 33 34 1 0 33 33 (1,42]
13 13 13 1 1 36 37 1 0 36 36 (1,42]
14 14 14 1 1 39 40 1 0 39 39 (1,42]
15 15 15 1 1 42 43 1 0 42 42 (1,42]
16 16 16 1 4 0 4 2 3 0 3 [0,1]
17 17 17 1 4 3 7 2 3 3 6 (1,42]
18 18 18 1 4 6 10 2 3 6 9 (1,42]
19 19 19 1 4 9 13 2 3 9 12 (1,42]
20 20 20 1 4 12 16 2 3 12 15 (1,42]
21 21 21 1 4 15 19 2 3 15 18 (1,42]
22 22 22 1 4 18 22 2 3 18 21 (1,42]
23 23 23 1 4 21 25 2 3 21 24 (1,42]
24 24 24 1 4 24 28 2 3 24 27 (1,42]
25 25 25 1 4 27 31 2 3 27 30 (1,42]
26 26 26 1 4 30 34 2 3 30 33 (1,42]
27 27 27 1 4 33 37 2 3 33 36 (1,42]
28 28 28 1 4 36 40 2 3 36 39 (1,42]
29 29 29 1 4 39 43 2 3 39 42 (1,42]
30 30 30 1 7 0 7 3 6 0 6 [0,1]
31 31 31 1 7 3 10 3 6 3 9 (1,42]
32 32 32 1 7 6 13 3 6 6 12 (1,42]
33 33 33 1 7 9 16 3 6 9 15 (1,42]
34 34 34 1 7 12 19 3 6 12 18 (1,42]
35 35 35 1 7 15 22 3 6 15 21 (1,42]
36 36 36 1 7 18 25 3 6 18 24 (1,42]
37 37 37 1 7 21 28 3 6 21 27 (1,42]
38 38 38 1 7 24 31 3 6 24 30 (1,42]
39 39 39 1 7 27 34 3 6 27 33 (1,42]
40 40 40 1 7 30 37 3 6 30 36 (1,42]
41 41 41 1 7 33 40 3 6 33 39 (1,42]
42 42 42 1 7 36 43 3 6 36 42 (1,42]
43 43 43 1 10 0 10 4 9 0 9 [0,1]
44 44 44 1 10 3 13 4 9 3 12 (1,42]
45 45 45 1 10 6 16 4 9 6 15 (1,42]
46 46 46 1 10 9 19 4 9 9 18 (1,42]
47 47 47 1 10 12 22 4 9 12 21 (1,42]
48 48 48 1 10 15 25 4 9 15 24 (1,42]
49 49 49 1 10 18 28 4 9 18 27 (1,42]
50 50 50 1 10 21 31 4 9 21 30 (1,42]
51 51 51 1 10 24 34 4 9 24 33 (1,42]
52 52 52 1 10 27 37 4 9 27 36 (1,42]
53 53 53 1 10 30 40 4 9 30 39 (1,42]
54 54 54 1 10 33 43 4 9 33 42 (1,42]
55 55 55 1 13 0 13 5 12 0 12 [0,1]
56 56 56 1 13 3 16 5 12 3 15 (1,42]
57 57 57 1 13 6 19 5 12 6 18 (1,42]
58 58 58 1 13 9 22 5 12 9 21 (1,42]
59 59 59 1 13 12 25 5 12 12 24 (1,42]
60 60 60 1 13 15 28 5 12 15 27 (1,42]
61 61 61 1 13 18 31 5 12 18 30 (1,42]
62 62 62 1 13 21 34 5 12 21 33 (1,42]
63 63 63 1 13 24 37 5 12 24 36 (1,42]
64 64 64 1 13 27 40 5 12 27 39 (1,42]
65 65 65 1 13 30 43 5 12 30 42 (1,42]
66 66 66 1 16 0 16 6 15 0 15 [0,1]
67 67 67 1 16 3 19 6 15 3 18 (1,42]
68 68 68 1 16 6 22 6 15 6 21 (1,42]
69 69 69 1 16 9 25 6 15 9 24 (1,42]
70 70 70 1 16 12 28 6 15 12 27 (1,42]
71 71 71 1 16 15 31 6 15 15 30 (1,42]
72 72 72 1 16 18 34 6 15 18 33 (1,42]
73 73 73 1 16 21 37 6 15 21 36 (1,42]
74 74 74 1 16 24 40 6 15 24 39 (1,42]
75 75 75 1 16 27 43 6 15 27 42 (1,42]
76 76 76 1 19 0 19 7 18 0 18 [0,1]
77 77 77 1 19 3 22 7 18 3 21 (1,42]
78 78 78 1 19 6 25 7 18 6 24 (1,42]
79 79 79 1 19 9 28 7 18 9 27 (1,42]
80 80 80 1 19 12 31 7 18 12 30 (1,42]
81 81 81 1 19 15 34 7 18 15 33 (1,42]
82 82 82 1 19 18 37 7 18 18 36 (1,42]
83 83 83 1 19 21 40 7 18 21 39 (1,42]
84 84 84 1 19 24 43 7 18 24 42 (1,42]
85 85 85 1 22 0 22 8 21 0 21 [0,1]
86 86 86 1 22 3 25 8 21 3 24 (1,42]
87 87 87 1 22 6 28 8 21 6 27 (1,42]
88 88 88 1 22 9 31 8 21 9 30 (1,42]
89 89 89 1 22 12 34 8 21 12 33 (1,42]
90 90 90 1 22 15 37 8 21 15 36 (1,42]
91 91 91 1 22 18 40 8 21 18 39 (1,42]
92 92 92 1 22 21 43 8 21 21 42 (1,42]
93 93 93 1 25 0 25 9 24 0 24 [0,1]
94 94 94 1 25 3 28 9 24 3 27 (1,42]
95 95 95 1 25 6 31 9 24 6 30 (1,42]
96 96 96 1 25 9 34 9 24 9 33 (1,42]
97 97 97 1 25 12 37 9 24 12 36 (1,42]
98 98 98 1 25 15 40 9 24 15 39 (1,42]
99 99 99 1 25 18 43 9 24 18 42 (1,42]
100 100 100 1 28 0 28 10 27 0 27 [0,1]
101 101 101 1 28 3 31 10 27 3 30 (1,42]
102 102 102 1 28 6 34 10 27 6 33 (1,42]
103 103 103 1 28 9 37 10 27 9 36 (1,42]
104 104 104 1 28 12 40 10 27 12 39 (1,42]
105 105 105 1 28 15 43 10 27 15 42 (1,42]
106 106 106 1 31 0 31 11 30 0 30 [0,1]
107 107 107 1 31 3 34 11 30 3 33 (1,42]
108 108 108 1 31 6 37 11 30 6 36 (1,42]
109 109 109 1 31 9 40 11 30 9 39 (1,42]
110 110 110 1 31 12 43 11 30 12 42 (1,42]
111 111 111 1 34 0 34 12 33 0 33 [0,1]
112 112 112 1 34 3 37 12 33 3 36 (1,42]
113 113 113 1 34 6 40 12 33 6 39 (1,42]
114 114 114 1 34 9 43 12 33 9 42 (1,42]
115 115 115 1 37 0 37 13 36 0 36 [0,1]
116 116 116 1 37 3 40 13 36 3 39 (1,42]
117 117 117 1 37 6 43 13 36 6 42 (1,42]
118 118 118 1 40 0 40 14 39 0 39 [0,1]
119 119 119 1 40 3 43 14 39 3 42 (1,42]
120 120 120 1 43 0 43 15 42 0 42 [0,1]
INPUT --- group=1 S rows=15 cols=15 Triang ;
INPUT --- 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 2 ;
INPUT --- 1 2 2 2 2 ;
INPUT --- 1 2 2 2 ;
INPUT --- 1 2 2 ;
INPUT --- 1 2 ;
INPUT --- 1 ;
Output
When I run the model I get two different survivals, one referring to t1, and another one referring t4.
Parameter Estimate Standard Error Lower Upper
-------------------------- -------------- -------------- -------------- --------------
1:S g1 c1 a0 t1 0.9400895 0.0186263 0.8913805 0.9677459
2:S g1 c1 a3 t4 0.9968873 0.0020343 0.9888400 0.9991369
Is this a correct approach to deal with transients and resident survival estimates?
Thanks,
Sergi