Estimate basic parameters
estimate.RdEstimate singing rates, effective distances, and density based on simulation objects using the QPAD approach (Solymos et al. 2013).
Usage
estimate(object, ...)
# S3 method for class 'bsims_transcript'
estimate(object,
method = c("qpad", "sqpad", "convolution", "naive"), ...)Details
The method evaluates removal design to estimate model parameters and density using the QPAD and SQPAD methodologies using the 'detect' package. Convolution implements the full-information likelihood. The Navive estimator fits GLM assuming no detection error.
The function only works with multiple time and
distance intervals. It returns NA otherwise.
Value
A vector with values for singing rate (phi), effective detection distance (tau), density, and survey area.
References
Solymos, P., Matsuoka, S. M., Bayne, E. M., Lele, S. R., Fontaine, P., Cumming, S. G., Stralberg, D., Schmiegelow, F. K. A. & Song, S. J., 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution, 4: 1047–1058. <doi:10.1111/2041-210X.12106>
Solymos, P., Lele, S. R., 2025. Single bin QPAD (SQPAD) approach for robust analysis of point count data with detection error. Ornithological Applications, xx, xx–xx.
Examples
set.seed(2)
phi <- 0.5 # singing rate
tau <- 1 # EDR by strata
dur <- 10 # simulation duration
tbr <- c(2, 4, 6, 8, 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, 10) # 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)
estimate(x)
#> Loading required namespace: detect
#> density area cue_rate distance_param
#> 11.3640146 2.5740766 0.4754889 0.9051818