# Internals: plotting

This document illustrates how plotting is performed in `mrds`

. Simply plotting a detection function is relatively straight forward, however when adding covariates and double observer components, this can get complicated.

It’s assumed you have some familiarity with `mrds`

and a lot of familiarity with R. This is not supposed to be a replacement for the `mrds`

documentation, but rather further explanation for those hacking on `mrds`

.

The main point covered here at the moment is how the detection function is evaluated to plot the lines, not any of the other content in the plot.

## Terms of reference

- $y$ refers to distance: this could be perpendicular or radial.
- $g(y, \mathbf{z})$ a general detection function (missing the $\mathbf{z}$ if there are no covariates), we assume that the detection function has some parameters ($\mathbf{\theta}$, say) but they aren’t important here.
- $p_i = \int_0^w g(y,\mathbf{z}_i) \pi(y) \text{d}y$ (i.e. the
`fitted`

values of the detection function), where for line transects $\pi(y) = \frac{1}{w}$ and for point transects $\pi(y)=\frac{2y}{w^2}$.

## Single observer – `plot.ds`

### detection function only (`dsmodel`

), no covariates

When there are no covariates, the detection function is simply evaluated over the grid (between left and right truncations).

### detection function only (`dsmodel`

), with covariates

When covariates are present, the detection function is evaluated at a series of distances between left and right truncations (say `seq(left,width,100)`

). Then for each distance ($y_t$), the detection function is evaluated for each observed covariate combination ($\mathbf{z}_i$). Then the detection function values are averaged at each distance, weighted by the fitted probabilities of detection ($p_i$). For a given $y_t$, we evaluate:

## Double observer plots

All of `plot.io`

, `plot.io.fi`

, `plot.rem`

, `plot.rem.fi`

, `plot.trial`

and `plot.trial.fi`

call `plot_cond`

when making plots of the conditional detection function, which in turn calls `average.line.cond`

. They also each call `plot_uncond`

for plots of the unconditional detection function, this in turn calls `average.line`

.

*Note that for gamma detection function models (and presumably other models where $g(y,\mathbf{z})=1$ for $x \neq 0$), one must set the distance in the predictions in average.line to be the apex rather than zero (since this is where detection is assumed to be certain).*

Buckland et al (2004, Chapter 6) is the key reference. Pages 130-140 are most useful.

`average.line.cond`

Calculates the *conditional* detection function lines.

Calls `predict`

per distance (over a grid of distances, as above), then takes the mean per distance. This means that for each distance, the detection function is evaluated at all observed covariate combinations.

For `io`

, `trial`

and `rem`

methods, the predictions use the `$mr`

part of the model. It produces a detection function line for each observer for `io`

, `io.fi`

, `rem`

and `rem.fi`

methods, but only for the first observer for the `trial`

and `trial.fi`

methods.

`average.line`

Calculates the *unconditional* detection function lines.

As with `average.line.cond`

, predictions are made over a grid of distances for each covariate combinations. However, for the unconditional plots, the averages are weighted.

`average.line`

– Point independence models

For point independence models (`io`

, `rem`

and `trial`

), need to use the delta function given in Buckland et al (2004) p. 131, since we assume independence at a *point* (`y=0`

).

The `g0`

part of the delta depends on the model. (Note that `g0`

in the code is $\hat{p}^c_.(0,\mathbf{z})$ in equation (6.28), Buckland et al (2004), Chapter 6.)

Method | `g0` prediction model |
---|---|

`io` |
`model$mr` |

`rem` |
`model$mr` |

`trial` |
`model$mr$mr` |

So in the code, the lines:

```
detfct.pooled.values <- detfct(newdat$distance[newdat$observer==1],
ddfobj,width=model$meta.data$width-
model$meta.data$left)
deltax <- detfct.pooled.values/(cond.det$fitted/g0)
```

translate to the equations (6.27) and substituting in (6.28) in ADS:

So `deltax`

in the code is $1/\delta(y,\mathbf{z})$ in maths. We need $\delta$ since we can only estimate $\hat{p}^c_.$ (Buckland et al 2004, p 131).

Depending on observer, computes average values detection function values are calculated for the given distance using Buckland et al (2004) equation (6.50). Observer options are observer 1 or 2 (`obs==1`

or `obs==2`

), duplicates (`obs==3`

) or “pooled observation team” (`obs==4`

).

`obs==1`

:`sum(p1*deltax/prob.det)/sum(1/prob.det)`

`obs==2`

:`sum(p2*deltax/prob.det)/sum(1/prob.det)`

(not produced for`trial`

)`obs==3`

(duplicates):`sum(g0*detfct.pooled.values/prob.det)/sum(1/prob.det)`

- otherwise (i.e.
`obs==4`

, “pooled observer team”):`sum(p1*p2*deltax/prob.det)/sum(1/prob.det)`

`average.line`

– Full independence models

Things are simpler for the full independence model, since there is no $\delta$ to consider.

Again, depending on observer, computes (simple) average values based on observed covariates for a given distance. Observer options are observer 1 or 2 (`obs==1`

or `obs==2`

), duplicates (`obs==3`

) or “pooled observation team” (`obs==4`

).

`obs==1`

:`sum(p1/prob.det)/sum(1/prob.det)`

`obs==2`

:`sum(p2/prob.det)/sum(1/prob.det)`

(not produced for`trial.fi`

)`obs==3`

(duplicates):`sum(g0*detfct.pooled.values/prob.det)/sum(1/prob.det)`

- otherwise (i.e.
`obs==4`

, “pooled observer team”):`sum(p1*p2/prob.det)/sum(1/prob.det)`

## References

Buckland, ST, DR Anderson, KP Burnham, JL Laake, DL Borchers, and L Thomas. Advanced Distance Sampling, Oxford University Press, 2004.