wrong number of transects in the summary / data #84
Description
Transect generation appears correct
As does transect IDs within the generated transect shape object
> samplers@samplers
Simple feature collection with 243 features and 2 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 0.6181902 ymin: 0 xmax: 1973.233 ymax: 200
CRS: NA
First 10 features:
transect strata geometry
1 1 strat1 LINESTRING (0.6181902 0, 0....
2 2 strat1 LINESTRING (67.28486 0, 67....
3 3 strat1 LINESTRING (133.9515 0, 133...
4 4 strat1 LINESTRING (200.6182 0, 200...
5 5 strat1 LINESTRING (267.2849 0, 267...
6 6 strat1 LINESTRING (333.9515 0, 333...
7 7 strat1 LINESTRING (400.6182 0, 400...
8 8 strat1 LINESTRING (467.2849 0, 467...
9 9 strat1 LINESTRING (533.9516 0, 533...
10 10 strat1 LINESTRING (600.6182 0, 600...
Number of transects is displayed as around 100 for the segmented design
Number of samplers: 108
However in the simulation results - the mean.k is always 15 (this is correct for strat1 and strat2 but strat3 and strat4 should be ~100) ER se also seems to reflect the wrong k values
Summary Statistics
mean.Cover.Area mean.Effort mean.n mean.k mean.ER mean.se.ER sd.mean.ER
strat1 150000.0 1500.000 881.9329 15 0.5879553 0.08692732 0.01212480
strat2 150000.0 1500.000 880.8468 15 0.5872312 0.02220725 0.01098384
strat3 148419.2 1484.192 874.2893 15 0.5892201 0.04183990 0.03038126
strat4 148867.2 1488.672 873.5165 15 0.5867873 0.02015128 0.02426928
Total 597286.5 5972.865 3510.5856 15 0.5877985 0.02531741 0.01041120
Checking the survey data I can see that the transect IDs are wrong in there
> dat.strat3 <- dist.dat[dist.dat$Region.Label =="strat3",]
> length(unique(dat.strat1$Sample.Label))
[1] 15
Reproducible example
library(dsims)
BL <- matrix(c(0,0,1000,0,1000,100,0,100,0,0),ncol=2, byrow=TRUE)
BR <- matrix(c(1000,0,2000,0,2000,100,1000,100,1000,0),ncol=2, byrow=TRUE)
TL <- matrix(c(0,100,1000,100,1000,200,0,200,0,100),ncol=2, byrow=TRUE)
TR <- matrix(c(1000,100,2000,100,2000,200,1000,200,1000,100),ncol=2, byrow=TRUE)
pol1 <- sf::st_polygon(list(BL))
pol2 <- sf::st_polygon(list(BR))
pol3 <- sf::st_polygon(list(TL))
pol4 <- sf::st_polygon(list(TR))
sfc <- sf::st_sfc(pol1,pol2,pol3,pol4)
strata.names <- c("strat1", "strat2", "strat3", "strat4")
mp1 <- sf::st_sf(strata = strata.names, geom = sfc)
region <- make.region(region.name = "study.area",
strata.name = strata.names,
shape = mp1)
plot(region)
design <- make.design(region,
design = c("systematic", "systematic", "segmentedgrid", "segmentedgrid"),
#line.length = 6000,
spacing = c(66.66667, 66.66667, 28, 28),
seg.threshold = 0,
seg.length = 20,
truncation = 50)
samplers <- generate.transects(design, region)
plot(region, samplers)
samplers
density <- make.density(region,
x.space = 10,
constant = rep(1,4))
density <- add.hotspot(density,c(100,100),100,3)
density <- add.hotspot(density,c(500,100),50,1)
density <- add.hotspot(density,c(250,100),80,2)
density <- add.hotspot(density,c(400,100),80,1)
plot(density)
pop.desc <- make.population.description(region,
density,
N = rep(1000,4))
detect <- make.detectability()
analyses <- make.ds.analysis(truncation = 50)
sim <- make.simulation(reps = 3,
design,
pop.desc,
detect,
analyses)
eg.survey <- run.survey(sim)
plot(eg.survey)
sim <- run.simulation(sim)