df.cdf.estim {cvmgof} | R Documentation |
This function computes the local linear estimation of the conditional distribution function.
df.cdf.estim(x, y, data.X, data.Y, bandwidth, kernel.function = kernel.function.epan)
x |
a numeric vector. |
y |
a numeric vector. |
data.X |
a numeric data vector used to obtain the nonparametric estimator of the conditional distribution function. |
data.Y |
a numeric data vector used to obtain the nonparametric estimator of the conditional distribution function. |
bandwidth |
bandwidth used to obtain the nonparametric estimator of the conditional distribution function. |
kernel.function |
kernel function used to obtain the nonparametric estimator of the conditional distribution function. Default option is "kernel.function.epan" which corresponds to the Epanechnikov kernel function. |
Inappropriate bandwidth, x or y choices can produce "NaN" values in cumulative distribution function estimates.
Romain Azais, Sandie Ferrigno and Marie-Jose Martinez
G. R. Ducharme and S. Ferrigno. An omnibus test of goodness-of-fit for conditional distributions with applications to regression models. Journal of Statistical Planning and Inference, 142, 2748:2761, 2012.
R. Azais, S. Ferrigno and M-J Martinez. cvmgof: An R package for Cramér-von Mises goodness-of-fit tests in regression models. 2018. Preprint in progress.
set.seed(1) require(lattice) # Only for plotting conditional CDF # Data simulation n = 25 # Dataset size data.X = runif(n,min=0,max=5) # X data.Y = 0.2*data.X^2-data.X+2+rnorm(n,mean=0,sd=0.3) # Y ######################################################################## # Estimation of the link function bandwidth = 0.75 # Here, the bandwidth is arbitrarily fixed # Estimation of the conditional CDF xgrid = seq(0.5,4.5,by=0.1) ygrid = seq(-1,3,by=0.1) cdf_df = df.cdf.estim(xgrid,ygrid,data.X,data.Y,bandwidth) wireframe(cdf_df, drape=TRUE, col.regions=rainbow(100),zlab='CDF(y|x)',xlab='x',ylab='y',zlim=c(0,1.01))