| parzen {modeest} | R Documentation |
Parzen's kernel mode estimator is the value maximizing the kernel density estimate.
parzen(x,
bw = NULL,
kernel = "gaussian",
abc = FALSE,
par = shorth(x),
optim.method = "BFGS",
...)
x |
numeric. Vector of observations. |
bw |
numeric. The smoothing bandwidth to be used. |
kernel |
character. The kernel to be used. Available kernels are
|
abc |
logical. If |
par |
numeric. The initial value used in |
optim.method |
character. If |
... |
if |
If kernel = "uniform", the naive mode estimate is returned.
parzen returns a numeric value, the mode estimate. If abc = TRUE,
the x value maximizing the density estimate is returned. Otherwise,
the optim method is used to perform maximization, and the
attributes: 'value', 'counts', 'convergence' and 'message', coming from
the optim method, are added to the result.
The user should preferentially call parzen through
mlv(x, method = "kernel", ...) or mlv(x, method = "parzen", ...).
This returns an object of class mlv.
Presently, parzen is quite slow.
P. Poncet
Parzen E. (1962). On estimation of a probability density function and mode. Ann. Math. Stat., 33(3):1065–1076.
Konakov V.D. (1973). On the asymptotic normality of the mode of multidimensional distributions. Theory Probab. Appl., 18:794-803.
Eddy W.F. (1980). Optimum kernel estimators of the mode. Ann. Statist., 8(4):870-882.
Eddy W.F. (1982). The Asymptotic Distributions of Kernel Estimators of the Mode. Z. Wahrsch. Verw. Gebiete, 59:279-290.
Romano J.P. (1988). On weak convergence and optimality of kernel density estimates of the mode. Ann. Statist., 16(2):629-647.
Abraham C., Biau G. and Cadre B. (2003). Simple Estimation of the Mode of a Multivariate Density. Canad. J. Statist., 31(1):23-34.
Abraham C., Biau G. and Cadre B. (2004). On the Asymptotic Properties of a Simple Estimate of the Mode. ESAIM Probab. Stat., 8:1-11.
# Unimodal distribution x <- rlnorm(10000, meanlog = 3.4, sdlog = 0.2) ## True mode lnormMode(meanlog = 3.4, sdlog = 0.2) ## Estimate of the mode M <- mlv(x, method = "kernel", kernel = "gaussian", bw = 0.3, par = shorth(x)) print(M) plot(M)