diff --git a/MCDS-dot-exe-Report.Rmd b/MCDS-dot-exe-Report.Rmd index ba16b01..4b2ff67 100644 --- a/MCDS-dot-exe-Report.Rmd +++ b/MCDS-dot-exe-Report.Rmd @@ -13,12 +13,14 @@ editor_options: --- ```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -source("Support.R") +library(kableExtra) library(Distance) library(gt) -library(kableExtra) + +knitr::opts_chunk$set(echo = TRUE) re_eval <- FALSE + +source("Support.R") ``` ## Things outstanding in mrds @@ -26,34 +28,38 @@ re_eval <- FALSE In addition to the notes throughout this document the following also needs further work or investigations: -- Issue 83 in mrds relating to factor ordering differences between - mrds and MCDS +- **FIXED** Issue 83 in mrds relating to factor ordering differences + between mrds and MCDS () -- Reading in and processing the warnings and / or errors in the log +- **Now seems to spot and display warnings and errors from MCDS** Reading in and processing the warnings and / or errors in the log file generated by MCDS - What do we do about cases where mcds.exe fits with negative pdf? E.g. -- Do the monotonicity constraints get passed to MCDS correctly? +- Do the monotonicity constraints get passed to MCDS correctly? **YES** -- Check the passing of parameter starting values to MCDS - also - potential issues here regarding factor ordering! +- Check the passing of parameter starting values to MCDS - also + potential issues here regarding factor ordering! **Now passing parameter start values - no problems with factor ordering as this is dealt with by the reordering of the factor names passed to MCDS** -- Check the passing of parameter bound to MCDS - again potential for - factor ordering issues. Note this can only be done via mrds NOT via - Distance. +- **Made errors into non fatal warnings and messages** +- Check the passing of parameter bound to MCDS. Note this can only be done via mrds NOT via + Distance. Low priority - not done + +- Could do with more tests of the case of uniform only models to check that abundance estimates are correctly calculated. \newpage ## Capercaillie Data -Things that might want further investigation: +Various warnings and errors that want further investigation: +```{r warn_capercaillie, eval = re_eval} +# MCDS warning - does this want removed? +# ** Warning: One or more cluster sizes are coded as -1. Distance assumes -1 to mean a cluster of undetermined size. These observations are used for estimating detection probability and encounter rate, but not cluster size. ** + +``` -- When the R optimiser is selected both the lnl_R and lnl_MCDS values - appear to be the same in all cases. (After moving on to other - datasets this is not found to be the case.) ```{r capercaillie, eval = re_eval} data("capercaillie") @@ -88,13 +94,40 @@ Things that might want further investigation: - Nhat for the hn herm 1 model is \~14% higher for the MCDS optimised model than the R optimised model -- Why is the lnl_R value for the hr poly 2 model negative? +- Why is the lnl_R value for the hr poly 2 model negative? This has now changed! + +Various warnings and errors that want further investigation: +```{r warn_cuecount, eval = re_eval} +# Fitting hazard-rate key function with simple polynomial(4,6) adjustments +# ** Warning: One or more cluster sizes are coded as -1. Distance assumes -1 to mean a cluster of undetermined size. These observations are used for estimating detection probability and encounter rate, but not cluster size. ** +# Warning: Detection function is not strictly monotonic!Warning: Detection function is less than 0 at some distancesWarning: Detection function is not strictly monotonic! +# Warning: Detection function is less than 0 at some distancesAIC= -0.103 +# Fitting half-normal key function with Hermite(4,6) adjustments +# ** Warning: One or more cluster sizes are coded as -1. Distance assumes -1 to mean a cluster of undetermined size. These observations are used for estimating detection probability and encounter rate, but not cluster size. ** +# ** Warning: convergence failure ** +# Warning in check.mono(result, n.pts = control$mono.points) : +# Detection function is not strictly monotonic! +# Warning in check.mono(result, n.pts = control$mono.points) : +# Detection function is not strictly monotonic! +# AIC= -1.752 +# Warning in mrds::check.mono(model, n.pts = 20) : +# Detection function is not strictly monotonic! +# No survey area information supplied, only estimating detection function. +# +# Fitting half-normal key function with Hermite(4,6) adjustments +# ** Warning: One or more cluster sizes are coded as -1. Distance assumes -1 to mean a cluster of undetermined size. These observations are used for estimating detection probability and encounter rate, but not cluster size. ** +# ** Warning: convergence failure ** +# Warning in check.mono(result, n.pts = control$mono.points) : +# Detection function is not strictly monotonic! +# AIC= 1.966 +# No survey area information supplied, only estimating detection function. +``` ```{r cuecounting, eval = re_eval} data("CueCountingExample") model.compare <- test.models(CueCountingExample, - truncation = max(CueCountingExample$distance), + truncation = max(CueCountingExample$distance, na.rm = TRUE), transect = "point") save(model.compare, file = "results/cue_counting.ROBJ") @@ -119,22 +152,22 @@ knitr::kable(model.compare, Things that might want further investigation: -- Unhelpful error "Error in array(x, c(length(x), 1L), if - (!is.null(names(x))) list(names(x), : 'data' must be of a vector - type, was 'NULL' Error in t(partial) %\*% vcov : requires - numeric/complex matrix/vector arguments" - -- What should happen when you have a uniform with no adjustments??? - Shouldn't P always be 1? - p_MCDS is 1 for a few of these models and when it is, it is estimating Nhat much lower than the R optimiser. + +```{r warn_ducknest} +# Fitting half-normal key function with Hermite(4,6) adjustments +# Warning: First partial hessian is singular and second-partial hessian is NULL, no hessian +# AIC= 932.07 +# No hessian, possible numerical problems; only estimating detection function. +``` ```{r ducknest, eval = re_eval} data("ducknest") model.compare <- test.models(ducknest, - truncation = max(ducknest$distance), + truncation = max(ducknest$distance, na.rm = TRUE), transect = "line") save(model.compare, file = "results/ducknest.ROBJ") @@ -157,11 +190,20 @@ knitr::kable(model.compare, ## DuikerCameraTraps -Things that might want further investigation: - - Unhelpful error "Error -in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : -'data' must be of a vector type, was 'NULL' Error in t(partial) %\*% -vcov : requires numeric/complex matrix/vector arguments" - Some of the -Nhat values look to differ by around 20% between the two optimisers +Things that might want further investigation: +- Nhat values look to differ by around 20% between the two optimisers + + +```{r warn_duiker} +# Fitting half-normal key function with Hermite(4,6) adjustments +# Warning: First partial hessian is singular; using second-partial hessian +# Warning: First partial hessian is singular; using second-partial hessian +# AIC= 25014.184 +# Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : +# 'data' must be of a vector type, was 'NULL' +# Error in t(partial) %*% vcov : +# requires numeric/complex matrix/vector arguments +``` ```{r duiker, eval = re_eval} data("DuikerCameraTraps") @@ -199,7 +241,7 @@ truncation distance specified; using largest observed distance"? data("LTExercise") model.compare <- test.models(LTExercise, - truncation = max(LTExercise$distance), + truncation = max(LTExercise$distance, na.rm = TRUE), transect = "line") save(model.compare, file = "results/LTExercise.ROBJ") @@ -230,7 +272,7 @@ truncation distance specified; using largest observed distance"? data("PTExercise") model.compare <- test.models(PTExercise, - truncation = max(PTExercise$distance), + truncation = max(PTExercise$distance, na.rm = TRUE), transect = "point") save(model.compare, file = "results/PTExercise.ROBJ") @@ -404,22 +446,6 @@ knitr::kable(model.compare, ## Wren line transect -Some errors: - -```{r errors_wrenlt} -# Fitting half-normal key function with Hermite(4,6) adjustments -# AIC= 1417.081 -# Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : -# 'data' must be of a vector type, was 'NULL' -# Error in t(partial) %*% vcov : -# requires numeric/complex matrix/vector arguments -# Fitting half-normal key function with Hermite(4,6) adjustments -# AIC= 1417.081 -# Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : -# 'data' must be of a vector type, was 'NULL' -# Error in t(partial) %*% vcov : -# requires numeric/complex matrix/vector arguments -``` ```{r wren_lt, eval = re_eval} data("wren_lt") @@ -450,22 +476,6 @@ knitr::kable(model.compare, Some errors / warnings: -```{r errors_wrensnapshot} -# Warning in check.mono(result, n.pts = control$mono.points) : -# Detection function is less than 0 at some distances -# AIC= 2e+24 -# Warning in mrds::check.mono(model, n.pts = 20) : -# Detection function is less than 0 at some distances -# Some variance-covariance matrix elements were NA, possible numerical problems; only estimating detection function. -# ... -# Fitting half-normal key function with Hermite(4,6) adjustments -# AIC= 1069.234 -# Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : -# 'data' must be of a vector type, was 'NULL' -# Error in t(partial) %*% vcov : -# requires numeric/complex matrix/vector arguments -``` - ```{r wren_snapshot, eval = re_eval} data("wren_snapshot") @@ -493,6 +503,8 @@ knitr::kable(model.compare, ## dathr1 +Some estimates of abundance differ significantly! + ```{r dathr1, eval = re_eval} load(file = "data/dathr1.RData") @@ -549,26 +561,6 @@ knitr::kable(model.compare, Some errors / warnings: -```{r errors_dathr3} -# Fitting half-normal key function with Hermite(4,6) adjustments -# Error in -lt$value : invalid argument to unary operator -# In addition: Warning message: -# In system(paste0(path.to.MCDS.dot.exe, " 0, ", test.file$command.file.name), : -# running command 'C:/Users/lhm/AppData/Local/R/win-library/4.2/mrds/MCDS.exe 0, C:\Users\lhm\AppData\Local\Temp\Rtmp8YZzC4\cmdtmp8e5c1a9825d0.txt' had status 3 -# Error in if (lt$message == "FALSE CONVERGENCE") { : -# argument is of length zero -# -# -# All models failed to fit! -# -# Error in ds(dist.data, truncation = truncation, transect = transect, formula = ~1, : -# No models could be fitted. -# Fitting half-normal key function with Hermite(4,6) adjustments -# Error in -lt$value : invalid argument to unary operator -# In addition: Warning message: -# In system(paste0(path.to.MCDS.dot.exe, " 0, ", test.file$command.file.name), : -# running command 'C:/Users/lhm/AppData/Local/R/win-library/4.2/mrds/MCDS.exe 0, C:\Users\lhm\AppData\Local\Temp\Rtmp8YZzC4\cmdtmp8e5c16877002.txt' had status 3 -``` ```{r dathr3, eval = re_eval} load(file = "data/dathr3.RData") @@ -733,23 +725,12 @@ knitr::kable(model.compare, ``` - \newpage ## ETP Dolphins Some errors / warnings: -```{r errors_ETP} -# Fitting half-normal key function -# Error in -lt$value : invalid argument to unary operator -# -# -# All models failed to fit! -# -# Error in ds(dist.data, truncation = truncation, transect = transect, formula = models[[i]], : -# No models could be fitted. -``` ```{r ETP_Dolphin, eval = re_eval} data("ETP_Dolphin") @@ -775,8 +756,6 @@ knitr::kable(model.compare, ``` - - \newpage ## Minke data @@ -805,8 +784,7 @@ knitr::kable(model.compare, ``` -## Cluster Exercise - +## Cluster Exercise ```{r ClusterExercise, eval = re_eval} data("ClusterExercise") @@ -832,5 +810,3 @@ knitr::kable(model.compare, kable_styling(latex_options = "HOLD_position") ``` - - diff --git a/MCDS-dot-exe-Report.aux b/MCDS-dot-exe-Report.aux index 5e1d385..f1d6229 100644 --- a/MCDS-dot-exe-Report.aux +++ b/MCDS-dot-exe-Report.aux @@ -21,62 +21,62 @@ \newlabel{tab:capercaillie_results}{{1}{2}{Comparison of R and MCDS model fits for Capercaillie data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.1}{}} \@writefile{toc}{\contentsline {subsection}{Cue Counting Data}{3}{section*.3}\protected@file@percent } \newlabel{cue-counting-data}{{}{3}{Cue Counting Data}{section*.3}{}} -\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Comparison of R and MCDS model fits for cue counting data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model.}}{3}{table.2}\protected@file@percent } -\newlabel{tab:cuecounting_results}{{2}{3}{Comparison of R and MCDS model fits for cue counting data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.2}{}} -\@writefile{toc}{\contentsline {subsection}{Ducknest Data}{4}{section*.4}\protected@file@percent } -\newlabel{ducknest-data}{{}{4}{Ducknest Data}{section*.4}{}} -\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Comparison of R and MCDS model fits for Ducknest data. key - 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key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.20}{}} -\@writefile{toc}{\contentsline {subsection}{amakihi}{23}{section*.22}\protected@file@percent } -\newlabel{amakihi-1}{{}{23}{amakihi}{section*.22}{}} +\@writefile{toc}{\contentsline {subsection}{akepa data}{23}{section*.22}\protected@file@percent } +\newlabel{akepa-data}{{}{23}{akepa data}{section*.22}{}} \@writefile{lot}{\contentsline {table}{\numberline {21}{\ignorespaces Comparison of R and MCDS model fits for akepa data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model.}}{23}{table.21}\protected@file@percent } \newlabel{tab:akepa_results}{{21}{23}{Comparison of R and MCDS model fits for akepa data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.21}{}} -\gdef \@abspage@last{23} +\@writefile{toc}{\contentsline {subsection}{ETP Dolphins}{24}{section*.23}\protected@file@percent } +\newlabel{etp-dolphins}{{}{24}{ETP Dolphins}{section*.23}{}} +\@writefile{lot}{\contentsline {table}{\numberline {22}{\ignorespaces Comparison of R and MCDS model fits for ETP dolphin data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model.}}{24}{table.22}\protected@file@percent } +\newlabel{tab:ETP_Dolphin_results}{{22}{24}{Comparison of R and MCDS model fits for ETP dolphin data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.22}{}} +\@writefile{toc}{\contentsline {subsection}{Minke data}{25}{section*.24}\protected@file@percent } +\newlabel{minke-data}{{}{25}{Minke data}{section*.24}{}} +\@writefile{lot}{\contentsline {table}{\numberline {23}{\ignorespaces Comparison of R and MCDS model fits for minke data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model.}}{25}{table.23}\protected@file@percent } +\newlabel{tab:minke_results}{{23}{25}{Comparison of R and MCDS model fits for minke data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.23}{}} +\@writefile{toc}{\contentsline {subsection}{Cluster Exercise}{25}{section*.25}\protected@file@percent } +\newlabel{cluster-exercise}{{}{25}{Cluster Exercise}{section*.25}{}} +\@writefile{lot}{\contentsline {table}{\numberline {24}{\ignorespaces Comparison of R and MCDS model fits for ClusterExercise data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model.}}{25}{table.24}\protected@file@percent } +\newlabel{tab:ClusterExercise_results}{{24}{25}{Comparison of R and MCDS model fits for ClusterExercise data. key - key function, adj - type of adjustment term, nadj - number of adjustments, lnl\_R - likelihood value for R optimiser, lnl\_MCDS - likelihood value for MCDS optimiser, optimizer - the selected optimiser, p\_R the estimated average probability of detection for the R optimised model, p\_MCDS the estimated average probability of detection for the MCDS optimised model, Nhat\_R - estimated abundance in covered region from R optimised model, Nhat\_MCDS - estimated abundance in covered region from MCDS optimised model}{table.24}{}} +\gdef \@abspage@last{25} diff --git a/MCDS-dot-exe-Report.out b/MCDS-dot-exe-Report.out index 69cf86e..7387d80 100644 --- a/MCDS-dot-exe-Report.out +++ b/MCDS-dot-exe-Report.out @@ -19,4 +19,7 @@ \BOOKMARK [2][-]{section*.19}{\376\377\000r\000o\000c\000i\000o\000\137\000d\000a\000t}{}% 19 \BOOKMARK [2][-]{section*.20}{\376\377\000r\000o\000c\000i\000o\000\137\000d\000a\000t\0002}{}% 20 \BOOKMARK [2][-]{section*.21}{\376\377\000a\000m\000a\000k\000i\000h\000i}{}% 21 -\BOOKMARK [2][-]{section*.22}{\376\377\000a\000m\000a\000k\000i\000h\000i}{}% 22 +\BOOKMARK [2][-]{section*.22}{\376\377\000a\000k\000e\000p\000a\000\040\000d\000a\000t\000a}{}% 22 +\BOOKMARK [2][-]{section*.23}{\376\377\000E\000T\000P\000\040\000D\000o\000l\000p\000h\000i\000n\000s}{}% 23 +\BOOKMARK [2][-]{section*.24}{\376\377\000M\000i\000n\000k\000e\000\040\000d\000a\000t\000a}{}% 24 +\BOOKMARK [2][-]{section*.25}{\376\377\000C\000l\000u\000s\000t\000e\000r\000\040\000E\000x\000e\000r\000c\000i\000s\000e}{}% 25 diff --git a/MCDS-dot-exe-Report.pdf b/MCDS-dot-exe-Report.pdf index 5d543e1..e173a95 100644 Binary files a/MCDS-dot-exe-Report.pdf and b/MCDS-dot-exe-Report.pdf differ diff --git a/Support.R b/Support.R index 9c8e47f..f11eddc 100644 --- a/Support.R +++ b/Support.R @@ -17,11 +17,12 @@ test.models <- function(dist.data, truncation, transect = "line", lnl_R <- lnl_MCDS <- optimizer <- p_R <- p_MCDS <- Nhat_R <- Nhat_MCDS <- NULL for(i in seq(along = models$key)){ - # Fit model using only R optmizer + # Fit model using only R optimizer fit_R <- try(ds(dist.data, truncation = truncation, transect = transect, formula = ~1, + key = models$key[i], adjustment = models$adj[i], nadj = models$nadj[i], cutpoints = cutpoints, @@ -40,6 +41,7 @@ test.models <- function(dist.data, truncation, transect = "line", truncation = truncation, transect = transect, formula = ~1, + key = models$key[i], adjustment = models$adj[i], nadj = models$nadj[i], cutpoints = cutpoints, @@ -59,6 +61,7 @@ test.models <- function(dist.data, truncation, transect = "line", truncation = truncation, transect = transect, formula = ~1, + key = models$key[i], adjustment = models$adj[i], nadj = models$nadj[i], cutpoints = cutpoints, diff --git a/results/ClusterExercise.ROBJ b/results/ClusterExercise.ROBJ new file mode 100644 index 0000000..eb0f023 Binary files /dev/null and b/results/ClusterExercise.ROBJ differ diff --git a/results/DuikerCameraTraps.ROBJ b/results/DuikerCameraTraps.ROBJ index d0b4a16..53cca2a 100644 Binary files a/results/DuikerCameraTraps.ROBJ and b/results/DuikerCameraTraps.ROBJ differ diff --git a/results/ETP_Dolphin.ROBJ b/results/ETP_Dolphin.ROBJ new file mode 100644 index 0000000..c73730b Binary files /dev/null and b/results/ETP_Dolphin.ROBJ differ diff --git a/results/LTExercise.ROBJ b/results/LTExercise.ROBJ index 94498d4..172537b 100644 Binary files a/results/LTExercise.ROBJ and b/results/LTExercise.ROBJ differ diff --git a/results/PTExercise.ROBJ b/results/PTExercise.ROBJ index db2af00..24aacbd 100644 Binary files a/results/PTExercise.ROBJ and b/results/PTExercise.ROBJ differ diff --git a/results/Savannah_sparrow_1980.ROBJ b/results/Savannah_sparrow_1980.ROBJ index 50cbee0..1ad9642 100644 Binary files a/results/Savannah_sparrow_1980.ROBJ and b/results/Savannah_sparrow_1980.ROBJ differ diff --git a/results/Savannah_sparrow_1981.ROBJ b/results/Savannah_sparrow_1981.ROBJ index 3f7ece6..a49a16e 100644 Binary files a/results/Savannah_sparrow_1981.ROBJ and b/results/Savannah_sparrow_1981.ROBJ differ diff --git a/results/akepa.ROBJ b/results/akepa.ROBJ index a5b904d..71e26e3 100644 Binary files a/results/akepa.ROBJ and b/results/akepa.ROBJ differ diff --git a/results/amakihi.ROBJ b/results/amakihi.ROBJ index 8128ee5..0f9c2ff 100644 Binary files a/results/amakihi.ROBJ and b/results/amakihi.ROBJ differ diff --git a/results/capercaillie.ROBJ b/results/capercaillie.ROBJ index 8996108..a7e3cd5 100644 Binary files a/results/capercaillie.ROBJ and b/results/capercaillie.ROBJ differ diff --git a/results/cue_counting.ROBJ b/results/cue_counting.ROBJ index 7fff7a9..e50bdf6 100644 Binary files a/results/cue_counting.ROBJ and b/results/cue_counting.ROBJ differ diff --git a/results/dathr1.ROBJ b/results/dathr1.ROBJ index ba3fb67..c92c0e1 100644 Binary files a/results/dathr1.ROBJ and b/results/dathr1.ROBJ differ diff --git a/results/dathr2.ROBJ b/results/dathr2.ROBJ index ba3fb67..737272d 100644 Binary files a/results/dathr2.ROBJ and b/results/dathr2.ROBJ differ diff --git a/results/dathr3.ROBJ b/results/dathr3.ROBJ index cffe308..f166a89 100644 Binary files a/results/dathr3.ROBJ and b/results/dathr3.ROBJ differ diff --git a/results/dathr4.ROBJ b/results/dathr4.ROBJ index 399b70b..39e1abd 100644 Binary files a/results/dathr4.ROBJ and b/results/dathr4.ROBJ differ diff --git a/results/ducknest.ROBJ b/results/ducknest.ROBJ index d0b4a16..6caacd9 100644 Binary files a/results/ducknest.ROBJ and b/results/ducknest.ROBJ differ diff --git a/results/minke.ROBJ b/results/minke.ROBJ new file mode 100644 index 0000000..1841f89 Binary files /dev/null and b/results/minke.ROBJ differ diff --git a/results/rocio_dat.ROBJ b/results/rocio_dat.ROBJ index 27e62a7..afb9446 100644 Binary files a/results/rocio_dat.ROBJ and b/results/rocio_dat.ROBJ differ diff --git a/results/rocio_dat2.ROBJ b/results/rocio_dat2.ROBJ index 9187247..3b949dd 100644 Binary files a/results/rocio_dat2.ROBJ and b/results/rocio_dat2.ROBJ differ diff --git a/results/sikadeer.ROBJ b/results/sikadeer.ROBJ index 6e93d6b..3541f94 100644 Binary files a/results/sikadeer.ROBJ and b/results/sikadeer.ROBJ differ diff --git a/results/wren_5min.ROBJ b/results/wren_5min.ROBJ index f7b74d5..cf2bc15 100644 Binary files a/results/wren_5min.ROBJ and b/results/wren_5min.ROBJ differ diff --git a/results/wren_cuecount.ROBJ b/results/wren_cuecount.ROBJ index b2d629a..5bd3fcb 100644 Binary files a/results/wren_cuecount.ROBJ and b/results/wren_cuecount.ROBJ differ diff --git a/results/wren_lt.ROBJ b/results/wren_lt.ROBJ index fab79d8..bf7bf33 100644 Binary files a/results/wren_lt.ROBJ and b/results/wren_lt.ROBJ differ diff --git a/results/wren_snapshot.ROBJ b/results/wren_snapshot.ROBJ index 35b5d46..5c5308d 100644 Binary files a/results/wren_snapshot.ROBJ and b/results/wren_snapshot.ROBJ differ