noeffect.circ.lin | R Documentation |
Function noeffect.circ.lin
computes the no-effect test for a circular predictor variable and a real-valued response variable as described in Alonso-Pena et al. (2021). It uses the nonparametric Nadaraya-Watson estimator or the Local-Linear estimator for circular-linear data described in Di Marzio et al. (2009) and Oliveira et al. (2013).
Function noeffect.lin.circ
computes the no-effect test for a real-valued predictor variable and a circular response variable as described in Alonso-Pena et al. (2021). It uses the nonparametric Nadaraya-Watson estimator or the Local-Linear estimator for linear-circular data described in Di Marzio et al. (2012).
Function noeffect.circ.circ
computes the no-effect test for a circular predictor variable and a circular response variable as described in Alonso-Pena et al. (2021). It uses the nonparametric Nadaraya-Watson estimator or the Local-Linear estimator for circular-circular data described in Di Marzio et al. (2012).
noeffect.circ.lin(x, y, bw, method = "LL", calib = "chisq", n_boot = 500) noeffect.lin.circ(x, y, bw, method = "LL", n_boot = 500) noeffect.circ.circ(x, y, bw, method = "LL", n_boot = 500)
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 |
bw |
Smoothing parameter to be used. If not provided, functions |
method |
Character string giving the estimator to be used. This must be one of |
calib |
Character string giving the calibration method to be used in |
n_boot |
Number of bootstrap resamples. Default is |
See Alonso-Pena et al. (2021). The NAs will be automatically removed.
A list with class "htest"
containing the following components:
statistic |
observed value of the statistic. |
bw |
Smoothing parameter used. |
p.value |
p-value for the test. |
data.name |
a character string giving the name(s) of the data. |
alternative |
a character string describing the alternative hypothesis. |
Maria Alonso-Pena, Jose Ameijeiras-Alonso and Rosa M. Crujeiras
Alonso-Pena, M., Ameijeiras-Alonso, J. and Crujeiras, R.M. (2021) Nonparametric tests for circular regression. Journal of Statistical Computation and Simulation, 91(3), 477–500.
Di Marzio, M., Panzera A. and Taylor, C. C. (2009) Local polynomial regression for circular predictors. Statistics and Probability Letters, 79, 2066–2075.
Di Marzio, M., Panzera A. and Taylor, C. C. (2012) Non–parametric regression for circular responses. Scandinavian Journal of Statistics, 40, 228–255.
Oliveira, M., Crujeiras R.M. and Rodriguez-Casal, A. (2013) Nonparametric circular methods for exploring environmental data. Environmental and Ecological Statistics, 20, 1–17.
kern.reg.circ.lin
, kern.reg.lin.circ
, kern.reg.circ.circ
# No-effect circ-lin set.seed(2025) x <- rcircularuniform(200) y <- 2*sin(as.numeric(x)) + rnorm(200, sd=2) noeffect.circ.lin(x, y) # No-effect lin-circ set.seed(2025) x <- runif(200) y<- pi/8 + rvonmises(200, mu = 0, kappa = 0.75) noeffect.lin.circ(x, y) # No-effect circ-circ set.seed(2025) x <- rcircularuniform(200) y <- atan2(sin(2*x),cos(2*x)) + rvonmises(200, mu = 0, kappa = 2) noeffect.circ.circ(x, y)
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