simHDStr {AHMbook} | R Documentation |

## Simulate HDS time-removal or double-observer data

### Description

A general function for simulating hierarchical distance sampling (HDS) data combined with a time-removal (with 3 removal periods) or double-observer protocol, either for a line or a point transect protocol and with method = "removal" or method = "double". Also produces plots of the output.

At each site, it works with a strip of width `B*2`

(for line transects) or a circle of radius `B`

(for point transects).

The state process is simulated by first drawing a covariate value, "habitat", for each site from a Normal(0, 1) distribution. This is used in a log-linear regression with arguments `beta0`

and `beta1`

to calculate the expected number of groups in each strip or circle. Group size is simulated by first drawing from a Poisson distribution with parameter `lambda.group`

then adding 1.

For line transects, the distance from the line is drawn from a Uniform(0, B) distribution. For point transects, the distance from the point is simulated from B*sqrt(Uniform(0,1)), which ensures a uniform distribution over the circle.

The group size is used in a log-linear regression with arguments `alpha0`

and `alpha1`

to calculate the scale parameter, sigma, of the half-normal detection function. Detections are simulated according to the selected protocol.

To recreate the data sets used in the book with R 3.6.0 or later, include `sample.kind="Rounding"`

in the call to `set.seed`

. This should only be used for reproduction of old results.

### Usage

```
simHDStr(type = c("line", "point"), method=c("removal", "double"), nsites = 200,
lambda.group = 1, alpha0 = 0, alpha1 = 0, beta0 = 1, beta1 = 0.5,
p.avail = 0.75, K = 3, p.double = c(0.4, 0.6),
B = 3, discard0 = FALSE, show.plot = TRUE)
```

### Arguments

`type` |
The type of distance transect, either "line" or "point". |

`method` |
Is the method time-removal ("removal") or double-observer ("double")? |

`nsites` |
Number of sites (spatial replication) |

`lambda.group` |
Poisson mean of group size |

`alpha0` |
intercept of log-linear model relating sigma of half-normal detection function to group size |

`alpha1` |
slope of log-linear model relating sigma of half-normal detection function to group size |

`beta0` |
intercept of log-linear model relating the Poisson mean of the number of groups per unit area to habitat |

`beta1` |
slope of log-linear model relating the Poisson mean of the number of groups per unit area to habitat |

`p.avail` |
overall availability probability (phi in text) |

`K` |
number of removal periods (of equal length) |

`p.double` |
detection probability for first and second observer |

`B` |
strip half width or radius of the circle |

`discard0` |
whether to discard or keep the data from sites with nobody detected |

`show.plot` |
choose whether to show plots or not. Set to FALSE when running simulations. |

### Value

A list with the values of the arguments and the following additional elements:

`data` |
simulated distance sampling data: a matrix with a row for each group detected and 7 columns: site ID, status (1 if captured), x and y coordinates (NA for line transects), distance from the line or point, group size, the time interval of removal or capture history; if |

`habitat` |
simulated habitat covariate |

`M` |
simulated number of groups at each site |

`M.true` |
for point counts, the simulated number of groups within the circle sampled |

`params` |
a vector with the input arguments |

### Author(s)

Marc Kéry & Andy Royle

### References

Kéry, M. & Royle, J.A. (2016) *Applied Hierarchical Modeling in Ecology* AHM1 - 9.3.2 (time removal) and 9.4.1 (double observer).

### Examples

```
# Run with the default arguments and look at the structure of the output:
set.seed(123)
tmp <- simHDStr() # default: line, removal
str(tmp)
head(tmp$data)
tmp <- simHDStr("point", method="double")
str(tmp)
head(tmp$data)
```

*AHMbook*version 0.2.9 Index]