Nothing
# point transect exercise
# simulated data
data(PTExercise)
convert_units <- 0.01
context("PTexercise")
test_that("hn", {
# Effort : 30.00000
# # samples : 30
# Width : 20.00000
# # observations: 131
#
# Model
# Half-normal key, k(y) = Exp(-y**2/(2*A(1)**2))
#
#
# Point Standard Percent Coef. 95% Percent
# Parameter Estimate Error of Variation Confidence Interval
# --------- ----------- ----------- -------------- ----------------------
# D 70.822 11.134 15.72 51.976 96.502
# --------- ----------- ----------- -------------- ----------------------
#
# Measurement Units
# ---------------------------------
# Density: Numbers/hectares
# EDR: meters
#
# Component Percentages of Var(D)
# -------------------------------
# Detection probability : 59.3
# Encounter rate : 40.7
#
# half-normal model
#PTExercise$size <- 1
# df_hn <- ds(PTExercise, transect="point", key="hn", adjustment=NULL,
# truncation=20, convert.units=convert.units)
# df_hn <- dht2(df_hn, flatfile=PTExercise, strat_formula=~1)
# expect_equal(attr(df_hn,"density")$Density, 70.822, tol=1e-1)
# expect_equal(attr(df_hn,"density")$LCI, 51.976, tol=1e-2)
# expect_equal(attr(df_hn,"density")$UCI, 96.502, tol=1e-2)
# expect_equal(attr(df_hn,"density")$Density_se, 11.134, tol=1e-2)
# expect_equal(attr(df_hn,"density")$Density_CV, .1572, tol=1e-2)
})
# hazard
test_that("hn", {
df_hr <- ds(PTExercise, transect="point", key="hr", truncation=20,
convert_units=convert_units, er_var="P3")
# this gives answers per square metre
df_hr$dht
})
test_that("hn", {
df_unif <- ds(PTExercise, transect="point", key="unif", truncation=20,
convert_units=convert_units, order=1, er_var="P3")
# this gives answers per square metre
df_unif$dht
})
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