fitlincirc {activity} R Documentation

## Linear-circular regression

### Description

Fits a Von Mises kernel distribution describing a linear variable as a function of a circular predictor, and boostraps the null distribution in order to evaluate significance of radial variation in the linear variable.

### Usage

```fitlincirc(circdat, lindat, pCI = 0.95, reps = 10, res = 512)
```

### Arguments

 `circdat` Numeric vector of radian data matched with `lindat`. `lindat` Numeric vector of linear data matched with `circdat`. `pCI` Single numeric value between 0 and 1 defining proportional confidence interval to return. `reps` Integer number of bootstrap repetitions to perform. `res` Resolution of fitted distribution and null confidence interval - specifically a single integer number of points on the circular scale at which to record distributions.

### Details

Deviation of `lindat` from the null expecation is assessed either visually by the degree to which the fitted distribution departs from the null confidence interval (use generic plot function), or quantitatively by column `p` of slot `fit` in the resulting `lincircmod-class` object.

### Value

An object of type `lincircmod-class`

### References

Xu, H., Nichols, K. & Schoenberg, F.P. (2011) Directional kernel regression for wind and fire data. Forest Science, 57, 343-352.

### Examples

```#Example with reps limited to increase speed
data(BCIspeed)
i <- BCIspeed\$species=="ocelot"
sp <- log(BCIspeed\$speed[i])
tm <- BCIspeed\$time[i]*2*pi
mod <- fitlincirc(tm, sp, reps=50)
plot(mod, CircScale=24, xaxp=c(0,24,4), xlab="Time", ylab="log(speed)")
legend(8,-3, c("Fitted speed", "Null CI"), col=1:2, lty=1:2)
```

[Package activity version 1.3.1 Index]