I have a data set where I am interested in changes in lambda over time but I have years since detection as my x axis instead of a traditional time series date variable. I have tried running it in packaged segmented which works but I think changepoint is more what I am looking for in terms of assessing when the population goes from steady to period of decrease (loglambda<0) and back to increasing/steady. Below is an example of my data: lines are hand drawn to show the three separate expected means that I want to estimate the breakpoints for which here would be between 0 and 3.
I don't know how to do this in the package changepoint as it seems to want a timeseries type dataset.
Subset of data:
ysw lambdalog
1 12 0.0777704088
2 -1 0.0770295351
3 1 -0.7137508637
4 -21 0.1900608470
5 -1 -0.7246886455
6 -16 0.3981136917
7 -4 -0.4338304420
8 3 -1.2649336503
9 -25 0.0259855251
10 -23 0.3051055887
11 4 -0.0574958938
12 3 -0.1187091258
13 1 -1.1600080284
14 -7 0.1409314001
15 -9 -0.2445836355
16 0 0.2432861461
17 0 -0.2148360928
18 -1 0.0004340775
19 1 -0.5650091926
20 1 -0.1150294631
21 -17 0.0096000836
22 3 -1.1972262747
23 5 0.2816615496
24 0 0.0430262698
25 11 0.0004340775
26 2 -0.5214335044
27 -1 0.0884000848
28 3 0.3681628716
29 8 -0.0210967888
30 0 0.5352398525
31 3 -1.4642840300
32 1 0.0004340775
33 -1 0.0940950522
34 2 -0.3971574115
35 2 -0.9956786262
36 -6 0.3548977827
37 2 -0.4992292576
38 0 0.0461481798
39 4 0.0004340775
40 -1 0.0004340775
41 6 0.0004340775
42 3 -0.3001622741
43 1 -0.4860085597
44 -23 1.4471735416
45 -10 0.2247239238
46 1 0.5086513178
47 4 0.0181454897
48 -9 2.1820531165
49 1 -0.3709944386
50 4 -0.3001622741
51 -12 -0.0126803424
52 5 0.3012470886
53 1 0.3012470886
54 1 -0.6727233248
55 -17 0.0004340775
56 -2 0.0669668009
57 3 -0.2408783417
58 0 1.0414321647
59 -9 -0.1900392355
60 -19 0.2043913319
61 -3 -0.4884210574
62 0 0.1954389020
63 -1 -0.9422285548
64 1 -0.3550108256
65 -13 -0.1882981847
66 5 -0.0428658121
67 1 -0.4987729697
68 -3 0.3012470886
69 4 -0.4379410780
70 -30 0.8625317410
71 4 -0.1849711688
72 -1 -1.5635862431
73 3 -0.4053508494
74 -24 0.0577467655
75 1 -0.2189467475
76 12 -0.0464827200
77 -1 0.0040346161
78 0 -0.2277449898
79 0 -0.0669186751
80 -1 -0.1499012466
81 6 -0.2378090579
82 -30 -0.6241236143
83 0 0.0296131291
84 -5 0.7386399111
85 -2 0.2225126258
86 -4 0.0430055713
87 10 0.0685312345
88 0 -0.1587701220
89 -9 0.0191534873
90 0 -0.1754403054
91 2 -0.0997964995
92 -4 0.0004340775
93 -23 -0.2814436084
94 6 0.4066269892
95 -7 -0.0037022192
96 -15 0.0149178196
97 -15 -0.0469489904
98 5 -0.0836857170
99 -4 0.0004340775
100 -6 0.0905905045
101 1 0.2823624589
102 4 -0.3001622741
103 5 0.2590217867
104 1 -0.2008730497
105 0 0.3012470886
106 -2 0.1950389171
107 5 0.3012470886
108 -1 -0.4015469848
109 -1 0.2528176993
110 4 0.0004340775
111 -26 0.3599167362
112 -24 0.0076980924
113 -2 0.0141469324
114 -18 0.8759344724
115 13 0.0254955590
116 -11 -0.2157805125
117 2 1.5563145643
118 -5 0.0223833409
119 -3 -0.1297478663
120 10 0.0406414247
121 6 0.0255622223
122 2 -0.3512064532
123 5 -0.0822453776
124 -18 0.0756225421
125 5 0.0004340775
126 -3 0.3012470886
127 6 0.2469183638
128 6 -0.2272325059
129 0 0.1930297163
130 7 0.1324193887
131 -3 -0.0973868772
132 2 -0.1982838668
133 6 -0.0926580885
134 5 -0.3001622741
135 -5 0.0462757611
136 4 0.0004340775
137 2 -0.5893741528
138 -1 0.0004340775
139 -21 0.1234557127
140 -3 0.1394294585
141 -16 -0.2690270814
142 -4 0.3012470886
143 5 0.0004340775
144 0 1.5250577709
145 -5 0.0488432777
146 4 0.3935197874
147 -1 -0.0053934215
148 -22 0.5948283354
149 -15 0.1390473917
150 2 -0.1866357167
151 -4 -0.0430610621
152 2 -0.1892155438
153 9 -0.1499012466
154 3 -0.4214015022
155 -2 -0.0487318850
156 -23 0.0120547832
157 -1 -0.6683860010
158 -2 0.0894342011
159 -6 0.1151124632
160 2 -0.3361253830
161 6 -0.3880131261
162 -6 -0.1499012466
163 1 -0.6968039426
164 4 0.0004340775
165 -4 0.2115226048
166 5 -0.0330045563
167 0 0.1520380386
168 7 0.0004340775
169 8 -0.0055095496
170 4 -0.3001622741
171 -20 -0.0602602896
172 3 -0.9935177778
173 10 -0.0425250496
174 -4 0.0765606477
175 0 0.3012470886
176 -2 0.0966187956
177 0 0.0065857198
178 6 0.4772659954
179 -3 0.0004340775
180 -23 -0.1103640646
181 -18 0.2984908143
182 7 0.0590763119
183 -7 0.1319413467
184 3 -0.3001622741
185 4 -0.4108344721
186 0 0.1406326927
187 -2 -0.1864190114
188 3 0.0004340775
189 3 -0.1754403054
190 12 0.0678996305
191 6 -0.1187091258
192 -8 -0.0025173908
193 5 0.0004340775
194 -6 -0.0027023076
195 3 -0.3791701837
196 -4 -0.4301529323
197 1 -0.7755532697
198 7 0.3012470886
199 -9 0.0292625869
library(changepoint)
fit_changepoint = cpt.meanvar(a1$lambdalog)
# Return estimates
c(ints = param.est(fit_changepoint)$mean,
cp = cpts(fit_changepoint))
plot(fit_changepoint)
I get:
The figure makes no sense regarding time. Is there a way to do this with my dataset?
