This vignette uses the function dht2 because that function knows how to incorporate multipliers such as cue rates and propogate the uncertainty in cue rate into overall uncertainty in density and abundance. Because there is uncertainty coming not only from encounter rate variability and uncertainty in detection function parameters, but also from cue rate variability, the relative contribution of each source of uncertainty is tablated. This is the last table produced by printing the wren.estimate object. For the Montrave winter wren data, only 4% of the uncertainty in the density estimate is attributable to the detection function, 24% attributable to encounter rate variability and 71% attributable to between-individual variability in call rate.
+
This vignette uses the function dht2 because that function knows how to incorporate multipliers such as cue rates and propagate the uncertainty in cue rate into overall uncertainty in density and abundance. Because there is uncertainty coming not only from encounter rate variability and uncertainty in detection function parameters, but also from cue rate variability, the relative contribution of each source of uncertainty is tablated. This is the last table produced by printing the wren.estimate object. For the Montrave winter wren data, only 4% of the uncertainty in the density estimate is attributable to the detection function, 24% attributable to encounter rate variability and 71% attributable to between-individual variability in call rate.
This insight suggests that if this survey was to be repeated, exerting more effort in measuring between-individual variation in call rate would likely yield the most benefits in tightening the precision in density estimates.
Also note the poor fit of the model to the data; the P-value for the Cramer von-Mises test is <<0.05. This is caused by over-dispersion in the distribution of detected call distances. A single individual may sit on a tree branch and emit many song bursts, leading to a jagged distribution of call distances that is not well fitted by a smooth detection function. That over-dispersion will not bias the density estimates.
Fitting a simple dete
## Density:## Label Estimate se cv lcl ucl df## 1 Total 2.674253 0.2625745 0.09818612 2.206266 3.241509 598.5905
-
Visually inspect the fitted detection function with the plot() function, specifying the cutpoints histogram with argument breaks. Add the argument pdf so the plot shows the probability densiy function rather than the detection function. The probability density function is preferred for assessing model fit because the PDF incorporates information about the availability of animals to be detected. There are few animals available to be detected at small distances, therefore lack of fit at small distances is not as consequential for points as it is for lines (Figure 3).
+
Visually inspect the fitted detection function with the plot() function, specifying the cutpoints histogram with argument breaks. Add the argument pdf so the plot shows the probability density function rather than the detection function. The probability density function is preferred for assessing model fit because the PDF incorporates information about the availability of animals to be detected. There are few animals available to be detected at small distances, therefore lack of fit at small distances is not as consequential for points as it is for lines (Figure 3).
cutpoints<-c(0,5,10,15,20,30,40,max(Savannah_sparrow_1980$distance, na.rm=TRUE))plot(sasp.hn, breaks=cutpoints, pdf=TRUE, main="Savannah sparrow point transect data.")
histogram of radial detection distances is characteristically “humped” because few individuals are available to be detected near the points,
because of the hump shape (Figure 2), plotting to assess fit of data to detection distribution usually assessed via pdf=TRUE argument added to plot() function,
-
for the Arapaho National Refuge Savannah sparrow data, the three candidate models all provide adequeate fit to the data and produce comparable estimates of \(P_a\).
+
for the Arapaho National Refuge Savannah sparrow data, the three candidate models all provide adequate fit to the data and produce comparable estimates of \(P_a\).
Note there is a difference of 14 AIC units between the model using stratum-specific detection functions and the model using a pooled detection function, with the stratum-specific detection function model being preferrable. To be thorough, absolute goodness of fit for the three stratum-specific detection functions is checked, and all models fit the data adequately.
+
Note there is a difference of 14 AIC units between the model using stratum-specific detection functions and the model using a pooled detection function, with the stratum-specific detection function model being preferable. To be thorough, absolute goodness of fit for the three stratum-specific detection functions is checked, and all models fit the data adequately.
This vignette focuses upon use of stratum-specific detection functions as a model selection exercise. Consequently, the vignette does not examine stratum-specific abundance or density estimates. That output is not included in this example analysis, but can easily be produced by continuing the analysis begun in this example.
either truncation distance (numeric, e.g. 5) or percentage
-(as a string, e.g. "15%"). Can be supplied as a list with elements left
-and right if left truncation is required (e.g. list(left=1,right=20) or
-list(left="1%",right="15%") or even list(left="1",right="15%")). By
+(as a string, e.g. "15%","15"). Can be supplied as a list with elements
+left and right if left truncation is required (e.g. list(left=1,right=20)
+or list(left="1%",right="15%") or even list(left="1",right="15%")). By
default for exact distances the maximum observed distance is used as the
right truncation. When the data is binned, the right truncation is the
largest bin end point. Default left truncation is set to zero.
@@ -197,11 +197,10 @@
Argumentsdht_group
diff --git a/docs/reference/ducknest.html b/docs/reference/ducknest.html
index de873b8..cb85340 100644
--- a/docs/reference/ducknest.html
+++ b/docs/reference/ducknest.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/dummy_ddf.html b/docs/reference/dummy_ddf.html
index af1c3fe..22bc4fd 100644
--- a/docs/reference/dummy_ddf.html
+++ b/docs/reference/dummy_ddf.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/flatfile.html b/docs/reference/flatfile.html
index 9c3c0a3..0a36a43 100644
--- a/docs/reference/flatfile.html
+++ b/docs/reference/flatfile.html
@@ -21,7 +21,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/gof_ds.html b/docs/reference/gof_ds.html
index 7e3e07e..df46bc2 100644
--- a/docs/reference/gof_ds.html
+++ b/docs/reference/gof_ds.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/golftees.html b/docs/reference/golftees.html
index 3c096c2..96bddd9 100644
--- a/docs/reference/golftees.html
+++ b/docs/reference/golftees.html
@@ -23,7 +23,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 584caf9..c2f68a8 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -13,7 +13,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/logLik.dsmodel.html b/docs/reference/logLik.dsmodel.html
index ae67a75..4b07f57 100644
--- a/docs/reference/logLik.dsmodel.html
+++ b/docs/reference/logLik.dsmodel.html
@@ -13,7 +13,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/make_activity_fn.html b/docs/reference/make_activity_fn.html
index 19aff6e..78608e1 100644
--- a/docs/reference/make_activity_fn.html
+++ b/docs/reference/make_activity_fn.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/minke.html b/docs/reference/minke.html
index 9a478b7..5b56432 100644
--- a/docs/reference/minke.html
+++ b/docs/reference/minke.html
@@ -19,7 +19,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/p_dist_table.html b/docs/reference/p_dist_table.html
index 4e311d3..0eb268a 100644
--- a/docs/reference/p_dist_table.html
+++ b/docs/reference/p_dist_table.html
@@ -19,7 +19,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/plot.dsmodel.html b/docs/reference/plot.dsmodel.html
index a354066..a09f054 100644
--- a/docs/reference/plot.dsmodel.html
+++ b/docs/reference/plot.dsmodel.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/predict.dsmodel.html b/docs/reference/predict.dsmodel.html
index 230c802..a3f2ce2 100644
--- a/docs/reference/predict.dsmodel.html
+++ b/docs/reference/predict.dsmodel.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/predict.fake_ddf.html b/docs/reference/predict.fake_ddf.html
index ea80d94..8d2fc2f 100644
--- a/docs/reference/predict.fake_ddf.html
+++ b/docs/reference/predict.fake_ddf.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/print.dht_result.html b/docs/reference/print.dht_result.html
index 78df017..441a925 100644
--- a/docs/reference/print.dht_result.html
+++ b/docs/reference/print.dht_result.html
@@ -13,7 +13,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/print.dsmodel.html b/docs/reference/print.dsmodel.html
index a755911..9237cef 100644
--- a/docs/reference/print.dsmodel.html
+++ b/docs/reference/print.dsmodel.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/print.summary.dsmodel.html b/docs/reference/print.summary.dsmodel.html
index 98fff5c..c12c610 100644
--- a/docs/reference/print.summary.dsmodel.html
+++ b/docs/reference/print.summary.dsmodel.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/sikadeer.html b/docs/reference/sikadeer.html
index 836390c..5e991ab 100644
--- a/docs/reference/sikadeer.html
+++ b/docs/reference/sikadeer.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/summarize_ds_models.html b/docs/reference/summarize_ds_models.html
index 5ae0b9a..c9441e0 100644
--- a/docs/reference/summarize_ds_models.html
+++ b/docs/reference/summarize_ds_models.html
@@ -19,7 +19,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/summary.dht_bootstrap.html b/docs/reference/summary.dht_bootstrap.html
index 50c9c3f..ffebece 100644
--- a/docs/reference/summary.dht_bootstrap.html
+++ b/docs/reference/summary.dht_bootstrap.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/summary.dsmodel.html b/docs/reference/summary.dsmodel.html
index 6d9c3d7..b03788d 100644
--- a/docs/reference/summary.dsmodel.html
+++ b/docs/reference/summary.dsmodel.html
@@ -17,7 +17,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/unflatten.html b/docs/reference/unflatten.html
index b851623..801c7d5 100644
--- a/docs/reference/unflatten.html
+++ b/docs/reference/unflatten.html
@@ -19,7 +19,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/unimak.html b/docs/reference/unimak.html
index a362871..e83fa40 100644
--- a/docs/reference/unimak.html
+++ b/docs/reference/unimak.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/units_table.html b/docs/reference/units_table.html
index d2faf5e..7d60123 100644
--- a/docs/reference/units_table.html
+++ b/docs/reference/units_table.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/docs/reference/wren.html b/docs/reference/wren.html
index 2c31233..bdd3681 100644
--- a/docs/reference/wren.html
+++ b/docs/reference/wren.html
@@ -15,7 +15,7 @@
Distance
- 2.0.0.9010
+ 2.0.1
diff --git a/tests/testthat/test_variance.R b/tests/testthat/test_variance.R
index bc895cd..55cba1b 100644
--- a/tests/testthat/test_variance.R
+++ b/tests/testthat/test_variance.R
@@ -46,7 +46,7 @@ test_that("variance 2",{
observations=unflat$obs.table,
strat_formula=~1, convert_units=cu,
er_est="O2"),
- "Using O2 or O3 encounter rate variance estimator, assuming that sorting on Sample.Label is meaningful")
+ "Using one of O1, O2, O3, S1 or S2 encounter rate variance estimators, assuming that sorting on Sample.Label is meaningful.")
lr <- Nhat_O2[nrow(Nhat_O2), , drop=FALSE]
expect_equal(lr$Abundance, 1022, tol=1e-1)
diff --git a/vignettes/web-only/cues/cuecounts-distill.Rmd b/vignettes/web-only/cues/cuecounts-distill.Rmd
index f3f0008..ef1dd4f 100644
--- a/vignettes/web-only/cues/cuecounts-distill.Rmd
+++ b/vignettes/web-only/cues/cuecounts-distill.Rmd
@@ -144,7 +144,7 @@ Note the distinct lack of fit to the song data. This is because of many detecti
# Notes regarding the cue count estimates of Montrave winter wrens
-This vignette uses the function `dht2` because that function knows how to incorporate multipliers such as cue rates and propogate the uncertainty in cue rate into overall uncertainty in density and abundance. Because there is uncertainty coming not only from encounter rate variability and uncertainty in detection function parameters, but also from cue rate variability, the relative contribution of each source of uncertainty is tablated. This is the last table produced by printing the `wren.estimate` object. For the Montrave winter wren data, only 4% of the uncertainty in the density estimate is attributable to the detection function, 24% attributable to encounter rate variability and 71% attributable to between-individual variability in call rate.
+This vignette uses the function `dht2` because that function knows how to incorporate multipliers such as cue rates and propagate the uncertainty in cue rate into overall uncertainty in density and abundance. Because there is uncertainty coming not only from encounter rate variability and uncertainty in detection function parameters, but also from cue rate variability, the relative contribution of each source of uncertainty is tablated. This is the last table produced by printing the `wren.estimate` object. For the Montrave winter wren data, only 4% of the uncertainty in the density estimate is attributable to the detection function, 24% attributable to encounter rate variability and 71% attributable to between-individual variability in call rate.
This insight suggests that if this survey was to be repeated, exerting more effort in measuring between-individual variation in call rate would likely yield the most benefits in tightening the precision in density estimates.
diff --git a/vignettes/web-only/points/pointtransects-distill.Rmd b/vignettes/web-only/points/pointtransects-distill.Rmd
index 01e9ce0..28e5b9f 100644
--- a/vignettes/web-only/points/pointtransects-distill.Rmd
+++ b/vignettes/web-only/points/pointtransects-distill.Rmd
@@ -138,7 +138,7 @@ On calling the `ds` function, information is provided to the screen reminding th
summary(sasp.hn)
```
-Visually inspect the fitted detection function with the `plot()` function, specifying the cutpoints histogram with argument `breaks`. Add the argument `pdf` so the plot shows the probability densiy function rather than the detection function. The probability density function is preferred for assessing model fit because the PDF incorporates information about the availability of animals to be detected. There are few animals available to be detected at small distances, therefore lack of fit at small distances is not as consequential for points as it is for lines (Figure \@ref(fig:modelfit)).
+Visually inspect the fitted detection function with the `plot()` function, specifying the cutpoints histogram with argument `breaks`. Add the argument `pdf` so the plot shows the probability density function rather than the detection function. The probability density function is preferred for assessing model fit because the PDF incorporates information about the availability of animals to be detected. There are few animals available to be detected at small distances, therefore lack of fit at small distances is not as consequential for points as it is for lines (Figure \@ref(fig:modelfit)).
```{r, modelfit, fig.dim=c(7,5), fig.cap="Fit of half normal detection function to savannah sparrow data."}
cutpoints <- c(0,5,10,15,20,30,40,max(Savannah_sparrow_1980$distance, na.rm=TRUE))
@@ -206,6 +206,6 @@ Key differences between analysis of line transect data and point transect data
- argument `transect` in `ds()` must be set to `"point"`,
- histogram of radial detection distances is characteristically "humped" because few individuals are available to be detected near the points,
- because of the hump shape (Figure \@ref(fig:basichist)), plotting to assess fit of data to detection distribution usually assessed via `pdf=TRUE` argument added to `plot()` function,
-- for the Arapaho National Refuge Savannah sparrow data, the three candidate models all provide adequeate fit to the data and produce comparable estimates of $P_a$.
+- for the Arapaho National Refuge Savannah sparrow data, the three candidate models all provide adequate fit to the data and produce comparable estimates of $P_a$.
# References
\ No newline at end of file
diff --git a/vignettes/web-only/strata/strata-distill.Rmd b/vignettes/web-only/strata/strata-distill.Rmd
index feb9904..39cb1f9 100644
--- a/vignettes/web-only/strata/strata-distill.Rmd
+++ b/vignettes/web-only/strata/strata-distill.Rmd
@@ -137,7 +137,7 @@ Further exploration of analyses involving stratification can be found in the [ex
# Comments
-Note there is a difference of `r round(model.pooled.AIC$AIC - model.separate.AIC)` AIC units between the model using stratum-specific detection functions and the model using a pooled detection function, with the stratum-specific detection function model being preferrable. To be thorough, absolute goodness of fit for the three stratum-specific detection functions is checked, and all models fit the data adequately.
+Note there is a difference of `r round(model.pooled.AIC$AIC - model.separate.AIC)` AIC units between the model using stratum-specific detection functions and the model using a pooled detection function, with the stratum-specific detection function model being preferable. To be thorough, absolute goodness of fit for the three stratum-specific detection functions is checked, and all models fit the data adequately.
This vignette focuses upon use of stratum-specific detection functions as a model selection exercise. Consequently, the vignette does not examine stratum-specific abundance or density estimates. That output is not included in this example analysis, but can easily be produced by continuing the analysis begun in this example.