kernelMixedDensity: Density with conditioning on discrete and continuous...

In smoothemplik: Smoothed Empirical Likelihood

Density with conditioning on discrete and continuous variables

Description

Density with conditioning on discrete and continuous variables

Usage

kernelMixedDensity(
  x,
  by,
  xout = NULL,
  byout = NULL,
  weights = NULL,
  parallel = FALSE,
  cores = 1,
  preschedule = TRUE,
  ...
)

Arguments

x	A numeric vector, matrix, or data frame containing observations. For density, the points used to compute the density. For kernel regression, the points corresponding to explanatory variables.
by	A variable containing unique identifiers of discrete categories.
xout	A vector or a matrix of data points with ncol(xout) = ncol(x) at which the user desires to compute the weights, density, or predictions. In other words, this is the requested evaluation grid. If NULL, then x itself is used as the grid.
byout	A variable containing unique identifiers of discrete categories for the output grid (same points as xout)
weights	A numeric vector of observation weights (typically counts) to perform weighted operations. If null, rep(1, NROW(x)) is used. In all calculations, the total number of observations is assumed to be the sum of weights.
parallel	Logical: if TRUE, parallelises the calculation over the unique values of by. At this moment, supports only parallel::mclapply (therefore, will not work on Windows).
cores	Integer: the number of CPU cores to use. High core count = high RAM usage. If the number of unique values of 'by' is less than the number of cores requested, then, only length(unique(by)) cores are used.
preschedule	Logical: passed as mc.preschedule to mclapply.
...	Passed to kernelDensity.

Value

A numeric vector of the density estimate of the same length as nrow(xout).

Examples

# Estimating 3 densities on something like a panel
set.seed(1)
n <- 200
x <- c(rnorm(n), rchisq(n, 4)/4, rexp(n, 1))
by <- rep(1:3, each = n)
xgrid <- seq(-3, 6, 0.1)
out <- expand.grid(x = xgrid, by = 1:3)
fhat <- kernelMixedDensity(x = x, xout = out$x, by = by, byout = out$by)
plot(xgrid, dnorm(xgrid)/3, type = "l", bty = "n", lty = 2, ylim = c(0, 0.35),
     xlab = "", ylab = "Density")
lines(xgrid, dchisq(xgrid*4, 4)*4/3, lty = 2, col = 2)
lines(xgrid, dexp(xgrid, 1)/3, lty = 2, col = 3)
for (i in 1:3) {
  lines(xgrid, fhat[out$by == i], col = i, lwd = 2)
  rug(x[by == i], col = i)
}
legend("top", c("00", "10", "01", "11"), col = 2:5, lwd  = 2)

smoothemplik documentation built on Aug. 22, 2025, 1:11 a.m.