By the end of this exercise you should feel confident doing the following:
.gdb
filesdata.frame
in R to get it into the correct format for Distance
ds()
First need to load the requisite R libraries
library(rgdal)
## Loading required package: sp
## rgdal: version: 1.0-4, (SVN revision 548)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 1.11.2, released 2015/02/10
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.2/Resources/library/rgdal/gdal
## Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.2/Resources/library/rgdal/proj
## Linking to sp version: 1.1-1
library(ggplot2)
library(Distance)
## Loading required package: mrds
## This is mrds 2.1.14
## Built: R 3.2.0; ; 2015-07-30 10:07:19 UTC; unix
library(knitr)
The observations are located in a “geodatabase” we created in Arc. We want to pull out the “Sightings” table (called a “layer”) and make it into a data.frame
(so that it’s easier for R to manipulate).
distdata <- readOGR("Analysis.gdb", layer="Sightings")
## OGR data source with driver: OpenFileGDB
## Source: "Analysis.gdb", layer: "Sightings"
## with 137 features
## It has 6 fields
distdata <- as.data.frame(distdata)
We can check it has the correct format using head
:
head(distdata)
## Survey GroupSize SeaState Distance SightingTime SightingID
## 1 en04395 2 3.0 246.0173 2004/06/28 10:22:21 1
## 2 en04395 2 2.5 1632.3934 2004/06/28 13:18:14 2
## 3 en04395 1 3.0 2368.9941 2004/06/28 14:13:34 3
## 4 en04395 1 3.5 244.6977 2004/06/28 15:06:01 4
## 5 en04395 1 4.0 2081.3468 2004/06/29 10:48:31 5
## 6 en04395 1 2.4 1149.2632 2004/06/29 14:35:34 6
## coords.x1 coords.x2
## 1 -65.636 39.576
## 2 -65.648 39.746
## 3 -65.692 39.843
## 4 -65.717 39.967
## 5 -65.820 40.279
## 6 -65.938 40.612
The Distance
package expects certain column names to be used. Renaming is much easier to do in R than ArcGIS, so we do it here.
distdata$distance <- distdata$Distance
distdata$object <- distdata$SightingID
distdata$size <- distdata$GroupSize
Let’s see what we did:
head(distdata)
## Survey GroupSize SeaState Distance SightingTime SightingID
## 1 en04395 2 3.0 246.0173 2004/06/28 10:22:21 1
## 2 en04395 2 2.5 1632.3934 2004/06/28 13:18:14 2
## 3 en04395 1 3.0 2368.9941 2004/06/28 14:13:34 3
## 4 en04395 1 3.5 244.6977 2004/06/28 15:06:01 4
## 5 en04395 1 4.0 2081.3468 2004/06/29 10:48:31 5
## 6 en04395 1 2.4 1149.2632 2004/06/29 14:35:34 6
## coords.x1 coords.x2 distance object size
## 1 -65.636 39.576 246.0173 1 2
## 2 -65.648 39.746 1632.3934 2 2
## 3 -65.692 39.843 2368.9941 3 1
## 4 -65.717 39.967 244.6977 4 1
## 5 -65.820 40.279 2081.3468 5 1
## 6 -65.938 40.612 1149.2632 6 1
We now have four “extra” columns.
Before setting off fitting detection functions, let’s look at the relationship of various variables in the data.
Don’t worry too much about understanding the code that generates these plots at the moment.
Obviously, the most important covariate in a distance sampling analysis is distance itself. We can plot a histogram of the distances to check that (1) we imported the data correctly and (2) it conforms to the usual shape for line transect data.
hist(distdata$distance, xlab="Distance (m)", main="Distance to sperm whale observations")
We might expect that there will be a relationship between the distance at whcih we see animals and the size of the groups observed (larger groups are easier to see at larger distances), so let’s plot that to help us visualise the relationship.
# plot of size versus distance and sea state vs distance, linear model and LOESS smoother overlay
# put the data into a simple format, only selecting what we need
distplot <- distdata[,c("distance","size","SeaState")]
names(distplot) <- c("Distance", "Size", "Beaufort")
library(reshape2)
# "melt" the data to have only three columns (try head(distplot))
distplot <- melt(distplot, id.vars="Distance", value.name="covariate")
# make the plot
p <- ggplot(distplot, aes(x=covariate, y=Distance)) +
geom_point() +
facet_wrap(~variable, scale="free") +
geom_smooth(method="loess", se=FALSE) +
geom_smooth(method="lm", se=FALSE) +
labs(x="Covariate value", y="Distance (m)")
print(p)
We might also expect that increaing sea state would result in a drop in observations. We can plot histograms of distance for each sea state level (making the sea state take only values 0,1,2,4,5 for this).
distdata$SeaStateCut <- cut(distdata$SeaState,seq(0,5,by=1), include.lowest=TRUE)
p <- ggplot(distdata) +
geom_histogram(aes(distance)) +
facet_wrap(~SeaStateCut) +
labs(x="Distance (m)", y="Count")
print(p)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
Given we are including data from two different surveys we can also investigate the relationship between survey and distances observed.
p <- ggplot(distdata) +
geom_histogram(aes(distance)) +
facet_wrap(~Survey) +
labs(x="Distance (m)", y="Count")
print(p)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
It’s now time to fit some detection function models. We’ll use the ds()
function from the Distance
package to fit the detection function. You can access the help file for the ds()
function by typing ?ds
– this will give you information about what the different arguments to the function are and what they do.
We can fit a very simple model with the following code:
df_hn <- ds(data=distdata, truncation=6000, key="hn", adjustment=NULL)
## Fitting half-normal key function
## Key only models do not require monotonicity contraints. Not constraining model for monotonicity.
## AIC= 2252.06
## No survey area information supplied, only estimating detection function.
Let’s dissect the call and see what each argument means:
data=distdata
: the data to use to fit the model, as we prepared above.truncation=6000
: set the truncation distance. Here, observations at distances greater than 6000m will be discarded before fitting the detection function.key="hn"
: the key function to use for the detection function, in this case half-normal (?ds
lists the other options).adjustment=NULL
: adjustment term series to fit. NULL
here means that no adjustments should be fitted (again ?ds
lists all options).Other useful arguments for this practical are:
formula=
: gives the formula to use for the scale parameter. By default it takes the value ~1
, meaning the scale parameter is constant and not a function of covariates.order=
: specifies the “order” of the adjustments to be used. This is a number (or vector of numbers) specifying the order of the terms. For example order=2
fits order 2 adjustments, order=c(2,3)
will fit a model with order 2 and 3 adjustments (mathematically, it only makes sense to include order 3 with order 2). By default the value is NULL
which has ds()
select the number of adjustments by AIC.We can look at the summary of the fitted detection function using the summary()
function:
summary(df_hn)
##
## Summary for distance analysis
## Number of observations : 132
## Distance range : 0 - 6000
##
## Model : Half-normal key function
## AIC : 2252.06
##
## Detection function parameters
## Scale Coefficients:
## estimate se
## (Intercept) 7.900732 0.07884776
##
## Estimate SE CV
## Average p 0.5490484 0.03662569 0.06670757
## N in covered region 240.4159539 21.32287580 0.08869160
Goodness of fit quantile-quantile plot and test results can be accessed using the ddf.gof()
function:
ddf.gof(df_hn$ddf, asp=1)
##
## Goodness of fit results for ddf object
##
## Chi-square tests
## [0,545] (545,1.09e+03] (1.09e+03,1.64e+03] (1.64e+03,2.18e+03]
## Observed 33.000000 20.00000000 19.000000000 8.000000
## Expected 21.708156 20.84245788 19.213254554 17.005089
## Chisquare 5.873634 0.03405238 0.002366986 4.768669
## (2.18e+03,2.73e+03] (2.73e+03,3.27e+03] (3.27e+03,3.82e+03]
## Observed 9.000000 13.0000000 14.000000
## Expected 14.450499 11.7899695 9.235669
## Chisquare 2.055842 0.1241881 2.457737
## (3.82e+03,4.36e+03] (4.36e+03,4.91e+03] (4.91e+03,5.45e+03]
## Observed 3.000000 4.0000000 7.000000
## Expected 6.946241 5.0159948 3.477683
## Chisquare 2.241906 0.2057908 3.567525
## (5.45e+03,6e+03] Total
## Observed 2.00000000 132.00000
## Expected 2.31498601 132.00000
## Chisquare 0.04285822 21.37457
##
## P =0.011087 with 9 degrees of freedom
##
## Distance sampling Kolmogorov-Smirnov test
## Test statistic = 0.11192 P = 0.073241
##
## Distance sampling Cramer-von Mises test(unweighted)
## Test statistic = 0.39618 P = 0.073947
Note three things here: 1. We use the $ddf
element of the detection function object 2. We’re ignoring the \(\Chi^2\) test results, as they rely on binning the distances to calculate test statistics where as Cramer-von Mises and Kolmogorov-Smirnov tests do not. 3. Using asp=1
to set the aspect ratio to 1, so the plot is not distorted.
We can plot the models simply using the plot()
function:
plot(df_hn)
The dots on the plot indicate the distances where observations are. We can remove them (particularly useful for a model without covariates) using the additional argument showpoints=FALSE
(try this out!).
Now try fitting a few models and comparing them using AIC. Don’t try to fit all possible models, just try a selection (say, a hazard-rate, a model with adjustments and a couple with different covariates). You can also try out changing the truncation distance.
Here’s an example to work from. Some tips before you start:
Control+Enter
(on Windows/Linux; Command+Enter
on Mac).df_
to indicate that this is a detection function, then shortened forms of the model form and covariates, separated by underscores, but use what makes sense to you (and future you!).df_hr_ss_size <- ds(distdata, truncation=6000, adjustment=NULL, key="hr", formula=~SeaState+size)
## Fitting hazard-rate key function
## AIC= 2249.327
## No survey area information supplied, only estimating detection function.
Once you’ve got the hang of writing models and looking at the differences between them, you should move onto the next section.
Looking at the models individually can be a bit unwieldy – it’s nicer to put that data into a table and sort the table by the relevant statistic.
The code below will make a results table with relevant statistics for model selection in it. Don’t worry about how this code exactly works at the moment.
make_table <- function(models){
# this function extracts the model data for a single model (row)
extract_model_data <- function(model){
c(summary(model)$ds$key,
model$ddf$ds$aux$ddfobj$scale$formula,
model$ddf$criterion,
ddf.gof(model$ddf, qq=FALSE)$dsgof$CvM$p,
summary(model)$ds$average.p,
summary(model)$ds$average.p.se
)
}
# applying that to all the models then putting it into a data.frame
res <- as.data.frame(t(as.data.frame(lapply(models, extract_model_data))),
stringsAsFactors=FALSE)
# making sure the correct columns are numeric
res[,3] <- as.numeric(res[,3])
res[,4] <- as.numeric(res[,4])
res[,5] <- as.numeric(res[,5])
res[,6] <- as.numeric(res[,6])
# giving the columns names
colnames(res) <- c("Key function", "Formula", "AIC", "Cramer-von Mises $p$-value",
"$\\hat{P_a}$", "se($\\hat{P_a}$)")
# creating a new column for the AIC difference to the best model
res[["$\\Delta$AIC"]] <- res$AIC - min(res$AIC, na.rm=TRUE)
# ordering the model by AIC score
res <- res[order(res$AIC),]
# returning the data.frame
return(res)
}
The make_table()
function expects a list
of models as it’s input. We can do that with the two models that I fitted like so:
models <- list()
models$df_hn <- df_hn
models$df_hr_ss_size <- df_hr_ss_size
(You can add the models you fitted above into this list.)
Here is the resulting table from the code above, made using the kable
function from knitr
:
model_table <- make_table(models)
kable(model_table, digits=3)
Key function | Formula | AIC | Cramer-von Mises \(p\)-value | \(\hat{P_a}\) | se(\(\hat{P_a}\)) | \(\Delta\)AIC | |
---|---|---|---|---|---|---|---|
df_hr_ss_size | hr | ~SeaState + size | 2249.327 | 0.880 | 0.355 | 0.074 | 0.000 |
df_hn | hn | ~1 | 2252.060 | 0.074 | 0.549 | 0.037 | 2.733 |
Note that there is a considerable spike in our distance data. This may be down to observers guarding the trackline (spending too much time searching near zero distance). It’s therefore possible that the hazard-rate model is overfitting to this peak. So we’d like to investigate results from the half-normal model too and see what the effects are on the final spatial models.
Just for fun, let’s estimate abundance from these models using a Horvtiz-Thompson-type estimator.
We know the Horvitz-Thompson estimator has the following form: \[ \hat{N} = \frac{A}{a} \sum_{i=1}^n \frac{s_i}{p_i} \] we can calculate each part of this equation in R:
A
is the total area of the region we want to estimate abundance for. This was \(A=5.285e+11 m^2\).a
is the total area we surveyed. We know that the total transect length was 9,498,474m and the truncation distance. Knowing that then \(a=2wL\) we can calculate \(a\).df_hn$ddf$data$size
.predict(df_hn$ddf)$fitted
.We know that in general operations are vectorised in R, so calculating c(1, 2, 3)/c(4, 5, 6)
will give c(1/4, 2/5, 3/6)
so we can just divide the results of getting the \(s_i\) and \(p_i\) values and then use the sum()
function to sum them up.
Try out estimating abundance using the formula below using both df_hn
and your favourite model from above:
Note that in the solutions to this exercise (posted on the course website) I show how to use the function dht()
to estimate abundance (and uncertainty) for a detection function analysis. This involves some more complex data manipulation steps, so we’ve left it out here in favour of getting to grips with the mathematics.
It’s common, especially in marine surveys, for animals at zero distance to be missed by observers. There are several ways to deal with this issue. For now, we are just going to use a very simply constant correction factor to inflate the abundance.
From Palka (2006), we think that observations on the track line were such that \(g(0)=0.46\), we can apply that correction to our abundance estimate (in a very primitive way):
This kind of correction works fine when we have a single number to adjust by, in general we’d like to model the perception bias using “mark-recapture distance sampling” techniques.
Save your top few models in an RData
file, so we can load them up later on. We’ll also save the distance data we used to fit out models.
save(df_hn, df_hr_ss_size, # add you models here, followed by commas!
distdata,
file="df-models.RData")
You can check it worked by using the load()
function to recover the models.
Palka, D. (2006). Summer Abundance Estimates of Cetaceans in US North Atlantic Navy Operating Areas. Northeast Fisheries Science Center Reference Document 06-03. Available online here