View source: R/bw.joint.dpcirc.R
bw.joint.dpcirc | R Documentation |
Function bw.joint.dpcirc
provides the smoothing parameters for the nonparametric joint estimator of the mean and dispersion functions when the conditional density is a double Poisson. It performs a joint cross-validation search.
bw.joint.dpcirc(x, y, startvmu = NULL, startvgam = NULL, lower=c(0.05,0.05), upper=c(50,7),tol = 0.00001, maxit = 300)
x |
Vector of data for the independent variable. The object is coerced to class circular. |
y |
Vector of data for the dependent variable. This must be same length as x and should contain counts. |
startvmu |
Vector of length two containing the initial values for the parameters corresponding to the estimation of the mean. |
startvgam |
Vector of length two containing the initial values for the parameters corresponding to the estimation of the dispersion. |
lower, upper |
Vectors of length two with the |
tol |
Tolerance parameter for convergence in the numerical estimation. |
maxit |
Maximum number of iterations in the numerical estimation. |
See Alonso-Pena et al. (2022) for details.
The NAs will be automatically removed.
A vector of length two with the first component being the value of the smoothing parameter associated to the mean estimation and with the second component being the value of the smoothing parameter associated to the dispersion estimation.
Maria Alonso-Pena, Irene Gijbels and Rosa M. Crujeiras
Alonso-Pena, M., Gijbels, I. and Crujeiras, R.M. (2022). Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts. Under review.
kern.dpreg.circ
data(spikes) direction<-circular(spikes$direction,units="degrees") counts<-spikes$counts bw.joint.dpcirc(direction, counts, lower=c(0.5,0.5), upper=c(50,7))
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