simNpC {AHMbook} | R Documentation |

## Generate counts from a single population observed over T years under a binomial observation process

### Description

Generates counts from a single population observed over T years and which can be observed with or without imperfect detection. The goal is to focus on what happens with relative-abundance inference when temporal patterns in abundance are confounded with temporal patterns in detection probability. Hence, we can simulate a stable population or one with linear increase or decrease with specified start and end points, and around which there is Poisson noise. The observed counts are Binomial outcomes with a detection probability which can similarly be chosen to be constant or change linearly over time.

### Usage

```
simNpC(T = 20, expN = c(100, 75), dp = c(0.5, 0.5), show.plot = TRUE)
```

### Arguments

`T` |
The length of the time series. |

`expN` |
The expected abundance at start and end of period, linear trend. |

`dp` |
The detection probability at start and end of period, linear trend. |

`show.plot` |
Choose whether to show plots or not. Set to FALSE when using function in simulations. |

### Value

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

`lambda` |
T vector, expected abundance for each year. |

`p` |
T vector, detection probability (dp) for each year. |

`N` |
T vector, realized abundance. |

`C` |
T vector, observed counts. |

### Author(s)

Marc Kéry & Andy Royle

### References

Kéry, M. & Royle, J.A. (2021) *Applied Hierarchical Modeling in Ecology* AHM2 - 1.2.

### Examples

```
# Run with the default arguments and look at the structure of the output:
set.seed(123)
tmp <- simNpC()
str(tmp)
tmp$C
```

*AHMbook*version 0.2.9 Index]