circsizer.regression {NPCirc} | R Documentation |
This function plots the CircSiZer map for circular regression estimation based on circular kernel methods, as described in Oliveira et al. (2013). The CircSiZer is an extension of SiZer proposed by Chaudhuri and Marron (1999) to circular data.
circsizer.regression(x, y, bws=NULL, adjust=2, ngrid=150, alpha=0.05, B=500,
B2=250, log.scale=TRUE, display=TRUE)
x |
Vector of data for the independent variable. The object is coerced to class |
y |
Vector of data for the dependent variable. This must be same length as |
bws |
Vector of smoothing parameters. Values of |
adjust |
If |
ngrid |
Integer indicating the number of equally spaced angles between |
alpha |
Significance level for the CircSiZer map. Default |
B |
Integer indicating the number of bootstrap samples to estimate the standard deviation of the derivative estimator. Default |
B2 |
Integer indicating the number of bootstrap samples to compute the denominator in Step 2 of algorithm described in Oliveira et al. (2013).
Default |
log.scale |
Logical, if |
display |
Logical, if |
See Details Section of circsizer.density
.
The NAs will be automatically removed.
An object with class circsizer
whose underlying structure is a list containing the following components.
data |
Original dataset. |
ngrid |
Number of equally spaced angles where the derivative of the regression estimator is evaluated. |
bw |
Vector of smoothing parameters (given in |
log.scale |
Logical; if |
CI |
List containing: a matrix with lower limits fot the confidence intervals; a matrix with the lower limits of the confidence intervals; a matrix with the Effective Sample Size. Each row corresponds to each value of the smoothing parameter and each column corresponds to an angle. |
col |
Matrix containing the colors for plotting the CircSiZer map. |
If display==TRUE
, the function also returns the CircSiZer map for regression.
Mar?a Oliveira, Rosa M. Crujeiras and Alberto Rodr?guez–Casal
Chaudhuri, P. and Marron, J.S. (1999). SiZer for exploration of structures in curves, Journal of the American Statistical Association, 94, 807–823.
Oliveira, M., Crujeiras, R.M. and Rodr?guez–Casal (2014) CircSiZer: an exploratory tool for circular data. Environmental and Ecological Statistics, 21, 143–159.
Oliveira, M., Crujeiras R.M. and Rodr?guez–Casal, A. (2014) NPCirc: an R package for nonparametric circular methods. Journal of Statistical Software, 61(9), 1–26. https://www.jstatsoft.org/v61/i09/
## Not run:
set.seed(2012)
n <- 100
x <- seq(0,2*pi,length=n)
y <- sin(x)+sqrt(0.5)*rnorm(n)
circsizer.regression(circular(x), y, bws=seq(10,60,by=5))
## End(Not run)