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 |
Numeric vector of linear data matched with |
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)