Access nests, events, detections, and totals
getters.RdAccess nests, events, detections, abundance, and density from simulation objects.
Usage
get_nests(x, ...)
# S3 method for class 'bsims_population'
get_nests(x, ...)
get_events(x, ...)
# S3 method for class 'bsims_events'
get_events(x, ...)
get_detections(x, ...)
# S3 method for class 'bsims_detections'
get_detections(x, ...)
get_abundance(x, ...)
# S3 method for class 'bsims_population'
get_abundance(x, ...)
get_density(x, ...)
# S3 method for class 'bsims_population'
get_density(x, ...)
get_table(x, ...)
# S3 method for class 'bsims_transcript'
get_table(x,
type = c("removal", "visits"), ...)Details
get_nests extracts the nest locations.
get_events extracts the events.
get_detections extracts the detections.
get_abundance gets the realized total abundance (N),
get_density gets the realized average density (abundance/area: N/A).
get_table returns the removal or visits table.
Value
get_abundance and get_density
returns a non-negative numeric value.
get_nests returns a data frame with the following columns:
i individual identifier,
s spatial stratum (H: habitat, E: edge, R: road)
x and y are coordinates of the nest locations,
g is behavioral (mixture) group or NA.
get_events returns a data frame with the following columns:
x and y are locations of the individual at the time of the event,
t time of the event within the duration interval,
v indicator variable for vocal (1) vs. movement (0) event,
a direction for vocalization events (NA for movement) in degrees clockwise relative to north,
i individual identifier.
get_detections returns a data frame with the following columns:
x and y are locations of the individual at the time of the event,
t time of the event within the duration interval,
v indicator variable for vocal (1) vs. movement (0) event,
a direction for vocalization events (NA for movement) in degrees clockwise relative to north,
d distance from observer when detected (otherwise NA).
f indicates the angle between the bird's vocalization direction (column a) relative to the observer (the value is 0 for movement events by default),
i individual identifier,
j perceived individual identifier.
get_table returns a matrix with distance bands as rows
and time intervals as columns. The cell values are counts
if the individuals detected in a removal fashion (only new
individuals counter over the time periods) or in a multiple-visits
fashion (counting of individuals restarts in every time interval).
Examples
phi <- 0.5 # singing rate
tau <- 1:3 # EDR by strata
dur <- 10 # simulation duration
tbr <- c(3, 5, 10) # time intervals
rbr <- c(0.5, 1, 1.5, Inf) # counting radii
l <- bsims_init(10, 0.5, 1)# landscape
p <- bsims_populate(l, 1) # population
e <- bsims_animate(p, # events
vocal_rate=phi, duration=dur)
d <- bsims_detect(e, # detections
tau=tau)
x <- bsims_transcribe(d, # transcription
tint=tbr, rint=rbr)
## next locations
head(get_nests(p))
#> i s x y g
#> 1 1 H -1.992186 4.1429109 NA
#> 2 2 H -3.397745 0.3655374 NA
#> 3 3 H -3.619101 -0.5493512 NA
#> 4 4 H -2.372446 -3.3058973 NA
#> 5 5 H -2.850421 -4.2631502 NA
#> 6 6 H -2.972889 2.2975645 NA
head(get_nests(e))
#> i s x y g
#> 1 1 H -1.992186 4.1429109 G1
#> 2 2 H -3.397745 0.3655374 G1
#> 3 3 H -3.619101 -0.5493512 G1
#> 4 4 H -2.372446 -3.3058973 G1
#> 5 5 H -2.850421 -4.2631502 G1
#> 6 6 H -2.972889 2.2975645 G1
head(get_nests(d))
#> i s x y g
#> 1 1 H -1.992186 4.1429109 G1
#> 2 2 H -3.397745 0.3655374 G1
#> 3 3 H -3.619101 -0.5493512 G1
#> 4 4 H -2.372446 -3.3058973 G1
#> 5 5 H -2.850421 -4.2631502 G1
#> 6 6 H -2.972889 2.2975645 G1
head(get_nests(x))
#> i s x y g
#> 1 1 H -1.992186 4.1429109 G1
#> 2 2 H -3.397745 0.3655374 G1
#> 3 3 H -3.619101 -0.5493512 G1
#> 4 4 H -2.372446 -3.3058973 G1
#> 5 5 H -2.850421 -4.2631502 G1
#> 6 6 H -2.972889 2.2975645 G1
## abundance
get_abundance(p)
#> [1] 99
get_abundance(e)
#> [1] 99
get_abundance(d)
#> [1] 99
get_abundance(x)
#> [1] 99
## density
get_density(p)
#> [1] 0.99
get_density(e)
#> [1] 0.99
get_density(d)
#> [1] 0.99
get_density(x)
#> [1] 0.99
## events
head(get_events(e))
#> x y t v a i
#> 1 -1.416591 0.1742159 0.00480860 1 0 45
#> 2 2.606151 1.8374771 0.01074640 1 156 90
#> 3 1.984942 -0.5702172 0.01440594 1 54 99
#> 4 -4.822422 3.8523376 0.02045773 1 284 35
#> 5 3.942365 -4.3564158 0.02574542 1 336 83
#> 6 3.851393 -2.9408766 0.02627925 1 193 91
head(get_events(d))
#> x y t v a f i
#> 1 -1.416591 0.1742159 0.00480860 1 0 NA 45
#> 2 2.606151 1.8374771 0.01074640 1 156 NA 90
#> 3 1.984942 -0.5702172 0.01440594 1 54 NA 99
#> 4 -4.822422 3.8523376 0.02045773 1 284 NA 35
#> 5 3.942365 -4.3564158 0.02574542 1 336 NA 83
#> 6 3.851393 -2.9408766 0.02627925 1 193 NA 91
head(get_events(x))
#> x y t v a f i
#> 1 -1.416591 0.1742159 0.00480860 1 0 NA 45
#> 2 2.606151 1.8374771 0.01074640 1 156 NA 90
#> 3 1.984942 -0.5702172 0.01440594 1 54 NA 99
#> 4 -4.822422 3.8523376 0.02045773 1 284 NA 35
#> 5 3.942365 -4.3564158 0.02574542 1 336 NA 83
#> 6 3.851393 -2.9408766 0.02627925 1 193 NA 91
## detections
head(get_detections(d))
#> x y t v a d f i j
#> 8 -0.8422087 1.5804489 0.07937606 1 38 1.7908473 NA 42 42
#> 13 -0.2569150 0.1222174 0.10797022 1 189 0.2845038 NA 56 56
#> 17 -1.1826129 -0.4709415 0.17878431 1 28 1.2729333 NA 46 46
#> 22 -0.3118975 1.5639444 0.31610480 1 166 1.5947420 NA 51 51
#> 26 1.6701308 0.5072672 0.41213545 1 276 1.7454676 NA 72 72
#> 45 0.6549135 -2.4654480 0.76787103 1 73 2.5509500 NA 58 58
head(get_detections(x))
#> x y t v a d f i j
#> 8 -0.8422087 1.5804489 0.07937606 1 38 1.7908473 NA 42 42
#> 13 -0.2569150 0.1222174 0.10797022 1 189 0.2845038 NA 56 56
#> 17 -1.1826129 -0.4709415 0.17878431 1 28 1.2729333 NA 46 46
#> 22 -0.3118975 1.5639444 0.31610480 1 166 1.5947420 NA 51 51
#> 26 1.6701308 0.5072672 0.41213545 1 276 1.7454676 NA 72 72
#> 45 0.6549135 -2.4654480 0.76787103 1 73 2.5509500 NA 58 58
get_table(x, "removal")
#> 0-3min 3-5min 5-10min
#> 0-50m 2 0 0
#> 50-100m 1 0 0
#> 100-150m 1 0 0
#> 150+m 6 2 0
get_table(x, "visits")
#> 0-3min 3-5min 5-10min
#> 0-50m 2 0 1
#> 50-100m 1 1 0
#> 100-150m 1 1 3
#> 150+m 6 3 10