Access nests, events, detections, and totals
getters.RdAccess nests, events, detections, abundance, and density from simulation objects.
Usage
get_nests(x, ...)
# S3 method for bsims_population
get_nests(x, ...)
get_events(x, ...)
# S3 method for bsims_events
get_events(x, ...)
get_detections(x, ...)
# S3 method for bsims_detections
get_detections(x, ...)
get_abundance(x, ...)
# S3 method for bsims_population
get_abundance(x, ...)
get_density(x, ...)
# S3 method for bsims_population
get_density(x, ...)
get_table(x, ...)
# S3 method for bsims_transcript
get_table(x,
type = c("removal", "visits"), ...)Arguments
- x
simulation object.
- type
character, the type of table to return:
"removal"includes only new individuals as time progresses,"visits"counts individuals in each time interval independent of each other.- ...
other arguments passed to internal functions.
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 -4.315038 -1.9201875 NA
#> 2 2 H -2.462982 -4.3714492 NA
#> 3 3 H -1.806481 -1.9443110 NA
#> 4 4 H -2.811867 4.6403058 NA
#> 5 5 H -4.031108 -4.4926347 NA
#> 6 6 H -1.992186 0.1235921 NA
head(get_nests(e))
#> i s x y g
#> 1 1 H -4.315038 -1.9201875 G1
#> 2 2 H -2.462982 -4.3714492 G1
#> 3 3 H -1.806481 -1.9443110 G1
#> 4 4 H -2.811867 4.6403058 G1
#> 5 5 H -4.031108 -4.4926347 G1
#> 6 6 H -1.992186 0.1235921 G1
head(get_nests(d))
#> i s x y g
#> 1 1 H -4.315038 -1.9201875 G1
#> 2 2 H -2.462982 -4.3714492 G1
#> 3 3 H -1.806481 -1.9443110 G1
#> 4 4 H -2.811867 4.6403058 G1
#> 5 5 H -4.031108 -4.4926347 G1
#> 6 6 H -1.992186 0.1235921 G1
head(get_nests(x))
#> i s x y g
#> 1 1 H -4.315038 -1.9201875 G1
#> 2 2 H -2.462982 -4.3714492 G1
#> 3 3 H -1.806481 -1.9443110 G1
#> 4 4 H -2.811867 4.6403058 G1
#> 5 5 H -4.031108 -4.4926347 G1
#> 6 6 H -1.992186 0.1235921 G1
## abundance
get_abundance(p)
#> [1] 95
get_abundance(e)
#> [1] 95
get_abundance(d)
#> [1] 95
get_abundance(x)
#> [1] 95
## density
get_density(p)
#> [1] 0.95
get_density(e)
#> [1] 0.95
get_density(d)
#> [1] 0.95
get_density(x)
#> [1] 0.95
## events
head(get_events(e))
#> x y t v a i
#> 1 -0.3738905 0.1742159 0.00480860 1 0 44
#> 2 3.4598208 -3.0812244 0.01074640 1 156 89
#> 3 -2.2047155 -3.9809563 0.02045773 1 284 34
#> 4 2.8201059 3.0568185 0.02574542 1 336 82
#> 5 2.8591436 -1.0125381 0.02627925 1 193 90
#> 6 -1.1423269 1.5779129 0.02695094 1 327 43
head(get_events(d))
#> x y t v a f i
#> 1 -0.3738905 0.1742159 0.00480860 1 0 NA 44
#> 2 3.4598208 -3.0812244 0.01074640 1 156 NA 89
#> 3 -2.2047155 -3.9809563 0.02045773 1 284 NA 34
#> 4 2.8201059 3.0568185 0.02574542 1 336 NA 82
#> 5 2.8591436 -1.0125381 0.02627925 1 193 NA 90
#> 6 -1.1423269 1.5779129 0.02695094 1 327 NA 43
head(get_events(x))
#> x y t v a f i
#> 1 -0.3738905 0.1742159 0.00480860 1 0 NA 44
#> 2 3.4598208 -3.0812244 0.01074640 1 156 NA 89
#> 3 -2.2047155 -3.9809563 0.02045773 1 284 NA 34
#> 4 2.8201059 3.0568185 0.02574542 1 336 NA 82
#> 5 2.8591436 -1.0125381 0.02627925 1 193 NA 90
#> 6 -1.1423269 1.5779129 0.02695094 1 327 NA 43
## detections
head(get_detections(d))
#> x y t v a d f i j
#> 1 -0.37389047 0.1742159 0.00480860 1 0 0.4124867 NA 44 44
#> 7 -0.95018898 1.5035194 0.07937606 1 38 1.7786033 NA 41 41
#> 15 0.07908970 -0.4709415 0.17878431 1 28 0.4775365 NA 45 45
#> 17 0.72543673 1.4485678 0.20332589 1 164 1.6200639 NA 65 65
#> 18 1.01222174 1.7485858 0.20601417 1 25 2.0204319 NA 62 62
#> 21 0.05090871 -3.1189755 0.31610480 1 166 3.1193909 NA 50 50
head(get_detections(x))
#> x y t v a d f i j
#> 1 -0.37389047 0.1742159 0.00480860 1 0 0.4124867 NA 44 44
#> 7 -0.95018898 1.5035194 0.07937606 1 38 1.7786033 NA 41 41
#> 15 0.07908970 -0.4709415 0.17878431 1 28 0.4775365 NA 45 45
#> 17 0.72543673 1.4485678 0.20332589 1 164 1.6200639 NA 65 65
#> 21 0.05090871 -3.1189755 0.31610480 1 166 3.1193909 NA 50 50
#> 27 -1.99218646 0.1235921 0.39815399 1 330 1.9960165 NA 6 6
get_table(x, "removal")
#> 0-3min 3-5min 5-10min
#> 0-50m 2 0 0
#> 50-100m 0 0 0
#> 100-150m 2 0 0
#> 150+m 10 0 0
get_table(x, "visits")
#> 0-3min 3-5min 5-10min
#> 0-50m 2 1 2
#> 50-100m 0 0 0
#> 100-150m 2 1 2
#> 150+m 10 1 10