ssm projection

[otoolem]

Hi Ian

I want to use x.se and y.se, from the fit_ssm output to calculate the edge of the error ellipse around each data point.

grab output

out <- foieGras::fit_ssm(...)
ssm <- foieGras::grab(out, “predicted”, as_sf = FALSE)

track coordinates

ssm$lon # or ssm$x
ssm$lat # or ssm$y

find edges of ellipse

ssm$lon2 <- ssm$x+ssm$x.se
ssm$lat2 <- ssm$y+ssm$y.se
xy <- as.matrix(ssm[c('lon2', 'lat2')])
proj4string <- as.character(CRS('+init=EPSG:4326'))
ssm$lon2 <- project(xy, proj4string, inv=TRUE)[,1] # edge of ellipse along lon axis
ssm$lat2 <- project(xy, proj4string, inv=TRUE)[,2] # edge of ellipse along lat axis

However, the track data and coordinates of the 'ellipse boundaries' appear to have different projections.

ssm[,c('lon','lat','lon2','lat2')]
lon lat lon2 lat2
1 -26.01687 5.600394 75.76054 35587.20
2 -26.17290 5.237531 -55.06476 36493.35
3 -26.30081 4.958494 -10.90205 34711.58
4 -26.84735 4.559007 -136.02852 32288.30
5 -27.05270 4.411449 72.44271 32046.08
6 -27.31685 4.221786 -55.77494 30812.26
7 -27.58099 4.032076 -114.66851 28310.99
8 -27.87863 3.681396 43.44134 23356.09
9 -27.57547 3.946964 82.59072 27578.42
10 -26.80854 4.600780 121.77401 30990.43
11 -25.65469 5.361667 -146.98428 36572.13
12 -25.29457 5.653881 -74.58530 37407.11
13 -24.98268 5.881524 -81.03987 39731.08
14 -24.78388 6.113966 -20.48755 40574.67
15 -25.24602 6.174506 89.12844 41328.01
16 -25.69252 6.105850 -66.64888 40847.09
17 -25.80649 5.875334 -95.19035 40497.73
18 -25.91273 5.497413 111.19606 38681.79
19 -25.99331 5.248576 104.58948 36843.61
20 -26.32641 4.761293 147.82622 34288.65
21 -26.55639 4.434251 -84.89157 31936.47
22 -27.02737 3.970427 100.31919 29876.73
23 -27.49835 3.506338 -87.91668 27056.50
24 -27.96933 3.042016 -82.58127 23543.93
25 -28.07799 2.887690 -164.90769 22149.28
26 -27.53539 3.085527 -98.08894 23024.86
27 -27.31244 3.138100 -54.01810 22570.34
28 -27.35467 2.920236 166.46369 20728.95
29 -27.44570 2.669940 -101.61275 18425.00
30 -28.19315 2.157776 -163.50920 17065.89
31 -28.94061 1.645436 -50.06504 13281.10
32 -29.44954 1.324774 -54.04135 10655.61
33 -29.67303 1.185742 -102.63438 10013.83

Any help is greatly appreciated

Cheers

Malcolm

[camrinbraun]

@otoolem what was the solution here? i'd also like to use the SE metrics as an error estimate around predicted locations. ideally, i'd get the SE metrics into lat/lon space.