By the end of this practical, you should feel comfortable:

- Looking at how changing
`k`

changes smooths - Plotting residuals vs. covariates
- Fitting models to the residuals
- Using
`gam.check`

and`rqgam.check`

- Using
`obs_exp`

`library(Distance)`

`## Loading required package: mrds`

```
## This is mrds 2.2.2
## Built: R 4.0.0; ; 2020-06-08 11:39:43 UTC; unix
```

```
##
## Attaching package: 'Distance'
```

```
## The following object is masked from 'package:mrds':
##
## create.bins
```

`library(dsm)`

`## Loading required package: mgcv`

`## Loading required package: nlme`

`## This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.`

`## Loading required package: numDeriv`

```
## This is dsm 2.3.0
## Built: R 4.0.0; ; 2020-06-08 19:51:53 UTC; unix
```

```
library(ggplot2)
library(knitr)
```

Load the data and the fitted `dsm`

objects from the previous exercises:

```
load("spermwhale.RData")
load("dsms.RData")
load("df-models.RData")
```

We’re going to check the DSMs that we fitted in the previous practical, then save those that we think are good!

`k`

changes smoothsFirst checking that `k`

is big enough, we should really do this during model fitting, but we’ve separated this up for the practical exercises.

First look at the text output of `gam.check`

, are the values of `k'`

for your models close to the `edf`

in the outputted table. Here’s a silly example where I’ve deliberately set `k`

too small:

```
dsm_k_check_eg <- dsm(count ~ s(Depth, k=4),
df_hn, segs, obs,
family=tw())
```

```
## Warning in make.data(response, ddf.obj, segment.data, observation.data, : Some
## observations are outside of detection function truncation!
```

`gam.check(dsm_k_check_eg)`

```
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-1.66079e-07,1.171305e-07]
## (score 392.2823 & scale 5.216272).
## Hessian positive definite, eigenvalue range [0.8702242,302.9225].
## Model rank = 4 / 4
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(Depth) 3.0 2.9 0.78 0.13
```

**Note here again I’m using a “chunk” option to suppress the plots printed by gam.check**

Generally if the EDF is close to the value of `k`

you supplied, it is worth doubling `k`

and refitting to see what happens. You can always switch back to the smaller `k`

if there is little difference. The `?choose.k`

manual page can offer some guidance.

Continuing with that example, if we double `k`

:

```
dsm_k_check_eg <- dsm(count ~ s(Depth, k=8),
df_hn, segs, obs,
family=tw())
```

```
## Warning in make.data(response, ddf.obj, segment.data, observation.data, : Some
## observations are outside of detection function truncation!
```

`gam.check(dsm_k_check_eg)`

```
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-5.675949e-08,4.752562e-08]
## (score 392.2128 & scale 5.216315).
## Hessian positive definite, eigenvalue range [1.313321,300.6316].
## Model rank = 8 / 8
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(Depth) 7.00 4.47 0.78 0.15
```

We get something much more reasonable. Doubling again

```
dsm_k_check_eg <- dsm(count ~ s(Depth, k=16),
df_hn, segs, obs,
family=tw())
```

```
## Warning in make.data(response, ddf.obj, segment.data, observation.data, : Some
## observations are outside of detection function truncation!
```

`gam.check(dsm_k_check_eg)`