fitact {activity} | R Documentation |

## Fit activity model to time-of-day data

### Description

Fits kernel density to radian time-of-day data and estimates activity level from this distribution. Optionally: 1. bootstraps the distribution, in which case SEs and confidence limits are also stored for activity level and PDF; 2. weights the distribution; 3. truncates the distribution at given times.

### Usage

```
fitact(
dat,
wt = NULL,
reps = 999,
bw = NULL,
adj = 1,
sample = c("none", "data", "model"),
bounds = NULL,
show = TRUE
)
```

### Arguments

`dat` |
A numeric vector of radian time-of-day data. |

`wt` |
A numeric vector of weights for each |

`reps` |
Number of bootstrap iterations to perform. Ignored if |

`bw` |
Numeric value for kernel bandwidth. If NULL, calculated internally. |

`adj` |
Numeric bandwidth adjustment multiplier. |

`sample` |
Character string defining sampling method for bootstrapping errors (see details). |

`bounds` |
A two-element vector defining radian bounds at which to truncate. |

`show` |
Logical whether or not to show a progress bar while bootstrapping. |

### Details

When no `bounds`

are given (default), a circular kernel distribution is fitted using `dvmkern`

.
Otherwise, a normal kernel distribution is used, truncated at the values of `bounds`

, using `density2`

.

The bandwidth adjustment multiplier `adj`

is provided to allow
exploration of the effect of adjusting the internally calculated bandwidth on
accuracy of activity level estimates.

The alternative bootstrapping methods defined by `sample`

are:

`"none"`

: no bootstrapping`"data"`

: sample from the data`"model"`

: sample from the fitted probability density distribution

It's generally better to sample from the data, but sampling from the fitted distribution can sometimes provide more sensible confidence intervals when the number of observations is very small.

### Value

An object of type `actmod`

### Examples

```
#Fit without confidence limits
data(BCItime)
tm <- 2*pi*subset(BCItime, species=="brocket")$time
mod1 <- fitact(tm)
plot(mod1)
#Fit with confidence limits (limited reps to speed up)
mod2 <- fitact(tm, sample="data", reps=10)
plot(mod2)
#Fit weighted function to correct for detection radius 1.2 times higher
#by day than by night, assuming day between pi/2 (6 am) and pi*2/3 (6 pm)
weight <- 1/ifelse(tm>pi/2 & tm<pi*3/2, 1.2, 1)
mod3 <- fitact(tm, wt=weight)
plot(mod3)
#Overplot unweighted version for comparison
plot(mod1, add=TRUE, tline=list(col=2))
#Fit truncated function to consider only night time records,
#assuming night between pi*3/2 (6 pm) and pi/3 (6 am)
mod4 <- fitact(tm, bounds=c(pi*3/2, pi/2))
plot(mod4, centre="night")
```

*activity*version 1.3.4 Index]