Density surface modelling

Description

dsm implements spatial models for distance sampling data. Models for detectability can be fitted using packages mrds or Distance. dsm fits generalized additive models to spatially-referenced data. See Miller et al (2013) for an introduction.

Details

Further information on distance sampling methods and example code is available at http://distancesampling.org/R/.

For help with distance sampling and this package, there is a Google Group https://groups.google.com/forum/#!forum/distance-sampling.

A example analyses are available at http://examples.distancesampling.org.

References

Hedley, S. and S. T. Buckland. 2004. Spatial models for line transect sampling. JABES 9:181-199.

Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access)

Wood, S.N. 2006. Generalized Additive Models: An Introduction with R. CRC/Chapman & Hall.

Find the block information

Description

Takes the transect data and works out how many blocks of a given size (in segment terms) fit into each.

Usage

block.info.per.su(block.size, data, name.su)

Arguments

Value

a data.frame with the following columns

• name the sample unit name (e.g. transect label)

• num.seg number of segments in that transect

• num.block number of blocks available

• start.block block number for first block

• end.block block number for last block

• num.req number of blocks needed for the unit

Check column names exist

Description

Internal function to check that supplied data.frames have the correct columns and checks that sample labels are all unique.

Usage

check.cols(ddf.obj, segment.data, observation.data, segment.area)

Arguments

Value

nothing, but throws an error if something went wrong

Author(s)

David Lawrence Miller

Fit a density surface model to segment-specific estimates of abundance or density.

Description

Fits a density surface model (DSM) to detection adjusted counts from a spatially-referenced distance sampling analysis. dsm takes observations of animals, allocates them to segments of line (or strip transects) and optionally adjusts the counts based on detectability using a supplied detection function model. A generalized additive model, generalized mixed model or generalized linear model is then used to model these adjusted counts based on a formula involving environmental covariates.

Usage

dsm(
  formula,
  ddf.obj,
  segment.data,
  observation.data,
  engine = "gam",
  convert.units = 1,
  family = quasipoisson(link = "log"),
  group = FALSE,
  control = list(keepData = TRUE),
  availability = 1,
  segment.area = NULL,
  weights = NULL,
  method = "REML",
  ...
)

Arguments

Details

The response (LHS of formula) can be one of the following (with restrictions outlined below):

• count count in each segment

• abundance.est estimated abundance per segment, estimation is via a Horvitz-Thompson estimator

• density.est density per segment

The offset used in the model is dependent on the response:

• count area of segment multiplied by average probability of detection in the segment

• abundance.est area of the segment

• density zero

The count response can only be used when detection function covariates only vary between segments/points (not within). For example, weather conditions (like visibility or sea state) or foliage cover are usually acceptable as they do not change within the segment, but animal sex or behaviour will not work. The abundance.est response can be used with any covariates in the detection function.

In the density case, observations can be weighted by segment areas via the ⁠weights=⁠ argument. By default (weights=NULL), when density is estimated the weights are set to the segment areas (using segment.area or by calculated from detection function object metadata and Effort data). Alternatively weights=1 will set the weights to all be equal. A third alternative is to pass in a vector of length equal to the number of segments, containing appropriate weights.

A example analyses are available at http://examples.distancesampling.org.

Value

a glm, gam, gamm or bam object, with an additional element, ⁠$ddf⁠ which holds the detection function object.

Units

It is often the case that distances are collected in metres and segment lengths are recorded in kilometres. dsm allows you to provide a conversion factor (convert.units) to multiply the areas by. For example: if distances are in metres and segment lengths are in kilometres setting convert.units=1000 will lead to the analysis being in metres. Setting convert.units=1/1000 will lead to the analysis being in kilometres. The conversion factor will be applied to segment.area if that is specified.

Large models

For large models, engine="bam" with method="fREML" may be useful. Models specified for bam should be as gam. Read bam before using this option; this option is considered EXPERIMENTAL at the moment. In particular note that the default basis choice (thin plate regression splines) will be slow and that in general fitting is less stable than when using gam. For negative binomial response, theta must be specified when using bam.

Author(s)

David L. Miller

References

Hedley, S. and S. T. Buckland. 2004. Spatial models for line transect sampling. JABES 9:181-199.

Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4: 1001-1010. doi: 10.1111/2041-210X.12105 (Open Access)

Wood, S.N. 2006. Generalized Additive Models: An Introduction with R. CRC/Chapman & Hall.

Examples

## Not run: 
library(Distance)
library(dsm)

# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)

# fit a detection function and look at the summary
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)
summary(hr.model)

# fit a simple smooth of x and y to counts
mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata)
summary(mod1)

# predict over a grid
mod1.pred <- predict(mod1, preddata, preddata$area)

# calculate the predicted abundance over the grid
sum(mod1.pred)

# plot the smooth
plot(mod1)

## End(Not run)

Data format for DSM

Description

Two data.frames must be provided to dsm. They are referred to as observation.data and segment.data.

Details

The segment.data table has the sample identifiers which define the segments, the corresponding effort (line length) expended and the environmental covariates that will be used to model abundance/density. observation.data provides a link table between the observations used in the detection function and the samples (segments), so that we can aggregate the observations to the segments (i.e., observation.data is a "look-up table" between the observations and the segments).

observation.data - the observation data.frame must have (at least) the following columns:

• object unique object identifier

• Sample.Label the identifier for the segment where observation occurred

• size the size of each observed group (e.g., 1 if all animals occurred individually)

• distance distance to observation

One can often also use observation.data to fit a detection function (so additional columns for detection function covariates are allowed in this table).

segment.data: the segment data.frame must have (at least) the following columns:

• Effort the effort (in terms of length of the segment)

• Sample.Label identifier for the segment (unique!)

• ??? environmental covariates, for example location (projected latitude and longitude), and other relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).

Multiple detection functions

If multiple detection functions are to be used, then a column named ddfobj must be included in observation.data and segment.data. This lets the model know which detection function each observation is from. These are numeric and ordered as the ddf.obj argument to dsm, e.g., ddf.obj=list(ship_ddf, aerial_ddf) means ship detections have ddfobj=1 and aerial detections have ddfobj=2 in the observation data.

Mark-recapture distance sampling models

When using mrds models that include mark-recapture components (currently independent observer and trial modes are supported) then the format of the observation data needs to be checked to ensure that observations are not duplicated. The observer column is also required in the observation.data.

• Independent observer mode only unique observations (unique object IDs) are required.

• Trial mode only observations made by observer 1 are required.

Check for autocorrelation in residuals

Description

This function is deprecated, use dsm_cor.

Usage

dsm.cor(
  dsm.obj,
  Transect.Label = "Transect.Label",
  Segment.Label = "Segment.Label",
  max.lag = 10,
  resid.type = "scaled.pearson",
  fun = cor,
  ylim = c(0, 1),
  subset = "all",
  ...
)

Arguments

Prediction variance estimation assuming independence

Description

This function is deprecated, use dsm_var_gam.

Usage

dsm.var.gam(
  dsm.obj,
  pred.data,
  off.set,
  seglen.varname = "Effort",
  type.pred = "response"
)

Arguments

Variance estimation via parametric moving block bootstrap

Description

This function is deprecated, use dsm_var_movblk.

Usage

dsm.var.movblk(
  dsm.object,
  pred.data,
  n.boot,
  block.size,
  off.set,
  ds.uncertainty = FALSE,
  samp.unit.name = "Transect.Label",
  progress.file = NULL,
  bs.file = NULL,
  bar = TRUE
)

Arguments

Prediction variance propagation for DSMs

Description

This function is deprecated, use dsm_var_prop.

Usage

dsm.var.prop(
  dsm.obj,
  pred.data,
  off.set,
  seglen.varname = "Effort",
  type.pred = "response"
)

Arguments

Check for autocorrelation in residuals

Description

Once a DSM has been fitted to data, this function can be used to check for autocorrelation in the residuals.

Usage

dsm_cor(
  dsm.obj,
  Transect.Label = "Transect.Label",
  Segment.Label = "Segment.Label",
  max.lag = 10,
  resid.type = "scaled.pearson",
  fun = cor,
  ylim = c(0, 1),
  subset = "all",
  ...
)

Arguments

Value

a plot or a vector of fun applied at the lags.

Details

Within each Transect.Label, segments will be sorted according to their Segment.Labels. This may require some time to get right for your particular data. If one has multiple surveys where transects are revisited, for example, one may want to make Transect.Label a unique transect-survey identifier. Neither label need to be included in the model, they must just be present in the ⁠$data⁠ field in the model. This usually means that they have to be in the segment data passed to dsm.

The current iteration of this function will only plot correlations nicely, other things are up to you but you can get the function to return the data (by assigning the result to an object).

If there are NA values in the residuals then the correlogram will not be calculated. This usually occurs due to NA values in the covariates (so the smoother will not have fitted values there). Code like any(is.na(dsm.obj$data)) might be helpful.

Author(s)

David L. Miller

Examples

 library(Distance)
 library(dsm)

 # load the data, see ?mexdolphins
 data(mexdolphins)

 # fit a model
 hr.model <- ds(distdata, truncation=6000,
                key = "hr", adjustment = NULL)
 mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata)

 # look at lag 1 differences up to a maximum of lag 9, using deviance
 # residuals
 dsm_cor(mod1, resid.type="deviance", max.lag=9,
         Segment.Label="Sample.Label")

Prediction variance estimation assuming independence

Description

If one is willing to assume the the detection function and spatial model are independent, this function will produce estimates of variance of predictions of abundance, using the result that squared coefficients of variation will add.

Usage

dsm_var_gam(
  dsm.obj,
  pred.data,
  off.set,
  seglen.varname = "Effort",
  type.pred = "response"
)

Arguments

Value

a list with elements

• model the fitted model object

• pred.var variance of the regions given in pred.data.

• bootstrap logical, always FALSE

• model the fitted model with the extra term

• dsm.object the original model (dsm.obj above)

Author(s)

David L. Miller

Examples

## Not run: 
 library(Distance)
 library(dsm)

 # load the Gulf of Mexico dolphin data (see ?mexdolphins)
 data(mexdolphins)

 # fit a detection function and look at the summary
 hr.model <- ds(distdata, truncation=6000,
                key = "hr", adjustment = NULL)
 summary(hr.model)

 # fit a simple smooth of x and y
 mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)

 # Calculate the variance
 # this will give a summary over the whole area in mexdolphins$preddata
 mod1.var <- dsm_var_gam(mod1, preddata, off.set=preddata$area)

## End(Not run)

Variance estimation via parametric moving block bootstrap

Description

Estimate the variance in abundance over an area using a moving block bootstrap. Two procedures are implemented, one incorporating detection function uncertainty, one not.

Usage

dsm_var_movblk(
  dsm.object,
  pred.data,
  n.boot,
  block.size,
  off.set,
  ds.uncertainty = FALSE,
  samp.unit.name = "Transect.Label",
  progress.file = NULL,
  bs.file = NULL,
  bar = TRUE
)

Arguments

Details

Setting ds.uncertainty=TRUE will incorporate detection function uncertainty directly into the bootstrap. This is done by generating observations from the fitted detection function and then re-fitting a new detection function (of the same form), then calculating a new effective strip width. Rejection sampling is used to generate the observations (except in the half-normal case) so the procedure can be rather slow. Note that this is currently not supported with covariates in the detection function.

Setting ds.uncertainty=FALSE will incorporate detection function uncertainty using the delta method. This assumes that the detection function and the spatial model are INDEPENDENT. This is probably not reasonable.

Examples

## Not run: 
library(Distance)
library(dsm)

# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)

# fit a detection function and look at the summary
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)
summary(hr.model)

# fit a simple smooth of x and y
mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata)
summary(mod1)

# calculate the variance by 500 moving block bootstraps
mod1.movblk <- dsm_var_movblk(mod1, preddata, n.boot = 500,
   block.size = 3, samp.unit.name = "Transect.Label",
   off.set = preddata$area,
   bar = TRUE, bs.file = "mexico-bs.csv", ds.uncertainty = TRUE)

## End(Not run)

Prediction variance propagation for DSMs

Description

To ensure that uncertainty from the detection function is correctly propagated to the final variance estimate of abundance, this function uses a method first detailed in Williams et al (2011), further explanation is given in Bravington et al. (2021).

Usage

dsm_var_prop(
  dsm.obj,
  pred.data,
  off.set,
  seglen.varname = "Effort",
  type.pred = "response"
)

Arguments

Details

The idea is to refit the spatial model but including an extra random effect. This random effect has zero mean and hence to effect on point estimates. Its variance is the Hessian of the detection function. Variance estimates then incorporate detection function uncertainty. Further mathematical details are given in the paper in the references below.

Many prediction grids can be supplied by supplying a list of data.frames to the function.

Note that this routine simply calls dsm_varprop. If you don't require multiple prediction grids, the other routine will probably be faster.

This routine is only useful if a detection function with covariates has been used in the DSM.

Value

a list with elements

• model the fitted model object

• pred.var variance of each region given in pred.data

• bootstrap logical, always FALSE

• pred.data as above

• off.set as above

• model the fitted model with the extra term

• dsm.object the original model, as above

• model.check simple check of subtracting the coefficients of the two models to see if there is a large difference

• deriv numerically calculated Hessian of the offset

Diagnostics

The summary output from the function includes a simply diagnostic that shows the average probability of detection from the "original" fitted model (the model supplied to this function; column Fitted.model) and the probability of detection from the refitted model (used for variance propagation; column Refitted.model) along with the standard error of the probability of detection from the fitted model (Fitted.model.se), at the unique values of any factor covariates used in the detection function (for continuous covariates the 5%, 50% and 95% quantiles are shown). If there are large differences between the probabilities of detection then there are potentially problems with the fitted model, the variance propagation or both. This can be because the fitted model does not account for enough of the variability in the data and in refitting the variance model accounts for this in the random effect.

Limitations

Note that this routine is only useful if a detection function has been used in the DSM. It cannot be used when the abundance.est or density.est responses are used. Importantly this requires that if the detection function has covariates, then these do not vary within a segment (so, for example covariates like sex cannot be used).

Author(s)

Mark V. Bravington, Sharon L. Hedley. Bugs added by David L. Miller.

References

Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation for Density Surface Models. Journal of Agricultural, Biological and Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2

Williams, R., Hedley, S.L., Branch, T.A., Bravington, M.V., Zerbini, A.N. and Findlay, K.P. (2011). Chilean Blue Whales as a Case Study to Illustrate Methods to Estimate Abundance and Evaluate Conservation Status of Rare Species. Conservation Biology 25(3), 526-535.

Variance propagation for density surface models

Description

Calculate the uncertainty in predictions from a fitted DSM, including uncertainty from the detection function.

Usage

dsm_varprop(
  model,
  newdata = NULL,
  trace = FALSE,
  var.type = "Vp",
  var_type = NULL
)

Arguments

Details

When we make predictions from a spatial model, we also want to know the uncertainty about that abundance estimate. Since density surface models are 2 (or more) stage models, we need to incorporate the uncertainty from the earlier stages (i.e. the detection function) into our "final" uncertainty estimate.

This function will refit the spatial model but include the Hessian of the offset as an extra term. Variance estimates using this new model can then be used to calculate the variance of predicted abundance estimates which incorporate detection function uncertainty. Importantly this requires that if the detection function has covariates, then these do not vary within a segment (so, for example covariates like sex cannot be used).

For more information on how to construct the prediction grid data.frame, newdata, see predict.dsm.

This routine is only useful if a detection function with covariates has been used in the DSM.

Note that we can use var.type="Vc" here (see gamObject), which is the variance-covariance matrix for the spatial model, corrected for smoothing parameter uncertainty. See Wood, Pya & S\"afken (2016) for more information.

Models with fixed scale parameters (e.g., negative binomial) do not require an extra round of optimisation.

Value

a list with elements:

• old_model fitted model supplied to the function as model

• refit refitted model object, with extra term

• pred point estimates of predictions at newdata

• var total variance calculated over all of newdata

• ses standard error for each prediction cell in newdata if newdata=NULL then the last three entries are NA.

Diagnostics

The summary output from the function includes a simply diagnostic that shows the average probability of detection from the "original" fitted model (the model supplied to this function; column Fitted.model) and the probability of detection from the refitted model (used for variance propagation; column Refitted.model) along with the standard error of the probability of detection from the fitted model (Fitted.model.se), at the unique values of any factor covariates used in the detection function (for continuous covariates the 5%, 50% and 95% quantiles are shown). If there are large differences between the probabilities of detection then there are potentially problems with the fitted model, the variance propagation or both. This can be because the fitted model does not account for enough of the variability in the data and in refitting the variance model accounts for this in the random effect.

Author(s)

David L. Miller, based on code from Mark V. Bravington and Sharon L. Hedley.

References

Bravington, M. V., Miller, D. L., & Hedley, S. L. (2021). Variance Propagation for Density Surface Models. Journal of Agricultural, Biological and Environmental Statistics. https://doi.org/10.1007/s13253-021-00438-2

Williams, R., Hedley, S.L., Branch, T.A., Bravington, M.V., Zerbini, A.N. and Findlay, K.P. (2011). Chilean Blue Whales as a Case Study to Illustrate Methods to Estimate Abundance and Evaluate Conservation Status of Rare Species. Conservation Biology 25(3), 526-535.

Wood, S.N., Pya, N. and S\"afken, B. (2016) Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 1-45.

Detection function objects when detection is certain

Description

Create a detection function object for strip/plot surveys for use in density surface models.

Usage

dummy_ddf(object, size = 1, width, left = 0, transect = "line")

Arguments

Author(s)

David L Miller

Generate data from a fitted detection function

Description

When using dsm.var.movblk if ds.uncertainty=TRUE, this procedure generates data from the fitted detection function (assuming that it is correct).

Usage

generate.ds.uncertainty(ds.object)

Arguments

Note

This function changes the random number generator seed. To avoid any potential side-effects, use something like: seed <- get(".Random.seed",envir=.GlobalEnv) before running code and assign(".Random.seed",seed,envir=.GlobalEnv) after.

Author(s)

David L. Miller

Moving block bootstrap sampler

Description

Not usually used on its own, called from within dsm.var.movblk.

Usage

generate.mb.sample(
  num.blocks.required,
  block.size,
  which.blocks,
  dsm.data,
  unit.info,
  n.units
)

Arguments

Value

vector of log-residuals

Convert latitude and longitude to Northings and Eastings

Description

Convert longitude and latitude co-ordinates to kilometres west-east and south-north from axes through (lon0,lat0) using the "spherical law of cosines".

Usage

latlong2km(lon, lat, lon0 = sum(range(lon))/2, lat0 = sum(range(lat))/2)

Arguments

Details

WARNING: This is an approximate procedure for converting between latitude/ longitude and Northing/Easting. Consider using projection conversions available in packages sp, sf and rgdal for better results.

Value

list with elements km.e and km.n.

Author(s)

Simon N. Wood

Create a knot grid for the internal part of a soap film smoother.

Description

This routine simply creates a grid of knots (in the correct format) to be used as in the "internal" part of the soap film smoother

Usage

make.soapgrid(bnd, n.grid)

Arguments

Value

a list with elements x and y, containing the knot locations.

Author(s)

David L Miller

Pan-tropical spotted dolphins in the Gulf of Mexico

Description

Data from a combination of several NOAA shipboard surveys conducted on pan-tropical spotted dolphins in the Gulf of Mexico. The data consist of 47 observations of groups of dolphins. The group size was recorded, as well as the Beaufort sea state at the time of the observation. Coordinates for each observation and bathymetry data were also available as covariates for the analysis. A complete example analysis (and description of the data) is provided at http://distancesampling.org/R/vignettes/mexico-analysis.html.

References

Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good, L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography 22(2):104-115

NOAA Southeast Fisheries Science Center. 1996. Report of a Cetacean Survey of Oceanic and Selected Continental Shelf Waters of the Northern Gulf of Mexico aboard NOAA Ship Oregon II (Cruise 220)

Observed versus expected diagnostics for fitted DSMs

Description

Given a covariate, calculate the observed and expected counts for each unique value of the covariate. This can be a useful goodness of fit check for DSMs.

Usage

obs_exp(model, covar, cut = NULL)

Arguments

Details

One strategy for model checking is to calculate observed and expected counts at different aggregations of the variable. If these match well then the model fit is good.

Value

data.frame with values of observed and expected counts.

Author(s)

David L Miller, on the suggestion of Mark Bravington.

Examples

## Not run: 
library(Distance)
library(dsm)

# example with the Gulf of Mexico dolphin data
data(mexdolphins)
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)
mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata)

## End(Not run)

Plot a density surface model.

Description

See plot.gam.

Usage

## S3 method for class 'dsm'
plot(x, ...)

Arguments

Value

a plot!

Author(s)

David L. Miller

See Also

dsm plot.gam

Create plots of abundance uncertainty

Description

Note that the prediction data set must have x and y columns even if these were not used in the model.

Usage

## S3 method for class 'dsm.var'
plot(
  x,
  poly = NULL,
  limits = NULL,
  breaks = NULL,
  legend.breaks = NULL,
  xlab = "x",
  ylab = "y",
  observations = TRUE,
  plot = TRUE,
  boxplot.coef = 1.5,
  x.name = "x",
  y.name = "y",
  gg.grad = NULL,
  ...
)

Arguments

Value

a plot

Details

In order to get plotting to work with dsm_var_prop and dsm_var_gam, one must first format the data correctly since these functions are designed to compute very general summaries. One summary is calculated for each element of the list pred supplied to dsm_var_prop and dsm_var_gam.

For a plot of uncertainty over a prediction grid, pred (a data.frame), say, we can create the correct format by simply using pred.new <- split(pred,1:nrow(pred)).

Author(s)

David L. Miller

See Also

dsm_var_prop, dsm_var_gam, dsm_var_movblk

Spatially plot predictions per model term

Description

Plot the effect of each smooth in the model spatially. For each term in the model, plot its effect in space. Plots are made on the same scale, so that the relative influence of each smooth can be seen.

Usage

plot_pred_by_term(dsm.obj, data, location.cov = c("x", "y"))

Arguments

Value

a ggplot2 plot

Author(s)

David L Miller (idea taken from inlabru)

Examples

## Not run: 
library(Distance)
library(dsm)

# load the Gulf of Mexico dolphin data and fit a model
data(mexdolphins)
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)
mod1 <- dsm(count~s(x,y) + s(depth), hr.model, segdata, obsdata)

preddata$width <- preddata$height <- sqrt(preddata$area)

# make the plot
plot_pred_by_term(mod1, preddata, c("x","y"))

# better plot would be
# library(viridis)
# plot_pred_by_term(mod1, preddata, c("x","y")) + scale_fill_viridis()

## End(Not run)

Predict from a fitted density surface model

Description

Make predictions of density or abundance outside (or inside) the covered area.

Usage

## S3 method for class 'dsm'
predict(object, newdata = NULL, off.set = NULL, type = "response", ...)

Arguments

Details

If newdata is not supplied, predictions are made for the data that built the model. Note that the order of the results will not necessarily be the same as the segdata (segment data) data.frame that was supplied to dsm.

The area off.set is used if that argument is supplied, otherwise it will look for the areas in the column named off.set in the newdata. Either way the link function (usually log) will be applied to the offsets, so there is no need to log them before passing them to this function.

Value

predicted values on the response scale by default (unless type is specified, in which case see predict.gam).

Author(s)

David L. Miller

See Also

predict.gam, dsm_var_gam, dsm_var_prop

Prediction for fake detection functions

Description

Prediction function for dummy detection functions. The function returns as many 1s as there are rows in newdata. If esw=TRUE then the strip width is returned.

Usage

## S3 method for class 'fake_ddf'
predict(
  object,
  newdata = NULL,
  compute = FALSE,
  int.range = NULL,
  esw = FALSE,
  ...
)

Arguments

Author(s)

David L Miller

Print a description of a density surface model object

Description

This method just gives a short description of the fitted model. Use the summary.dsm method for more information.

Usage

## S3 method for class 'dsm'
print(x, ...)

Arguments

Value

NULL

Author(s)

David L. Miller

Print a description of a density surface model variance object

Description

This method only provides a short summary, use the summary.dsm.var method for information.

Usage

## S3 method for class 'dsm.var'
print(x, ...)

Arguments

Value

NULL

Author(s)

David L. Miller

See Also

summary.dsm.var

Print a description of a density surface model variance object

Description

This method only provides a short summary, see summary.dsm_varprop.

Usage

## S3 method for class 'dsm_varprop'
print(x, ...)

Arguments

Author(s)

David L. Miller

See Also

summary.dsm_varprop

Print summary of density surface model variance object

Description

See summary.dsm.var for information.

Usage

## S3 method for class 'summary.dsm.var'
print(x, ...)

Arguments

Value

NULL

Author(s)

David L. Miller

See Also

summary.dsm.var

Print summary of density surface model variance object

Description

See summary.dsm_varprop for information.

Usage

## S3 method for class 'summary.dsm_varprop'
print(x, ...)

Arguments

Value

NULL

Author(s)

David L. Miller

See Also

summary.dsm.var

Randomised quantile residuals check plot for GAMs/DSMs

Description

This function is deprecated, use rqgam_check.

Usage

rqgam.check(gam.obj, ...)

Arguments

Randomised quantile residuals check plot for GAMs/DSMs

Description

Reproduces the "Resids vs. linear pred" plot from gam.check but using randomised quantile residuals, a la Dunn and Smyth (1996). Checks for heteroskedasticity as as usual, looking for "funnel"-type structures in the points, which is much easier with randomised quantile residuals than with deviance residuals, when your model uses a count distribution as the response.

Usage

rqgam_check(gam.obj, ...)

Arguments

Details

Note that this function only works with negative binomial and Tweedie response distributions.

Earlier versions of this function produced the full gam.check output, but this was confusing as only one of the plots was really useful. Checks of k are not computed, these need to be done using gam.check.

Value

just plots!

Author(s)

Based on code by Natalie Kelly, bugs added by Dave Miller

Examples

library(Distance)
library(dsm)
library(tweedie)

# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)

# fit a detection function and look at the summary
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)

# fit a simple smooth of x and y with a Tweedie response with estimated
#  p parameter
mod1 <- dsm(count~s(x, y), hr.model, segdata, obsdata, family=tw())
rqgam_check(mod1)

Summarize a fitted density surface model

Description

Gives a brief summary of a fitted dsm object.

Usage

## S3 method for class 'dsm'
summary(object, ...)

Arguments

Value

a summary object

Author(s)

David L. Miller

See Also

dsm

Summarize the variance of a density surface model

Description

Gives a brief summary of a fitted dsm variance object.

Usage

## S3 method for class 'dsm.var'
summary(
  object,
  alpha = 0.05,
  boxplot.coef = 1.5,
  bootstrap.subregions = NULL,
  ...
)

Arguments

Value

a summary object

Author(s)

David L. Miller

See Also

dsm.var.movblk, dsm.var.prop

Summarize the variance of a density surface model

Description

Gives a brief summary of a fitted dsm_varprop variance object.

Usage

## S3 method for class 'dsm_varprop'
summary(object, alpha = 0.05, ...)

Arguments

Value

a summary object

Author(s)

David L. Miller

See Also

dsm_varprop, summary.dsm.var

Trimmed variance

Description

Trim the variance estimates from the bootstrap. This is defined as the percentage defined as amount necessary to bring median and trimmed mean within 8% of each other these are defined as 'outliers'.

Usage

trim.var(untrimmed.bootstraps, boxplot.coef = 1.5)

Arguments

Value

trimmed variance

Author(s)

Louise Burt

Visualise concurvity between terms in a GAM

Description

This function is deprecated, use vis_concurvity.

Usage

vis.concurvity(model, type = "estimate")

Arguments

Visualise concurvity between terms in a GAM

Description

Plot measures of how much one term in the model could be explained by another. When values are high, one should consider re-running variable selection with one of the offending variables removed to check for stability in term selection.

Usage

vis_concurvity(model, type = "estimate")

Arguments

Details

These methods are considered somewhat experimental at this time. Consult concurvity for more information on how concurvity measures are calculated.

Author(s)

David L Miller

Examples

## Not run: 
library(Distance)
library(dsm)

# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)

# fit a detection function and look at the summary
hr.model <- ds(distdata, truncation=6000,
               key = "hr", adjustment = NULL)

# fit a simple smooth of x and y to counts
mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata)

# visualise concurvity using the "estimate" metric
vis_concurvity(mod1)

## End(Not run)