maxDiffCDF {logcondens} | R Documentation |
Compute maximal difference between CDFs corresponding to log-concave estimates
Description
Compute the maximal difference between two estimated log-concave distribution functions, either the MLEs or the smoothed versions. This function is used to set up a two-sample permutation test that assesses the null hypothesis of equality of distribution functions.
Usage
maxDiffCDF(res1, res2, which = c("MLE", "smooth"), n.grid = 500)
Arguments
res1 |
An object of class |
res2 |
An object of class |
which |
Indicate for which type of estimate the maximal difference should be computed. |
n.grid |
Number of grid points used to find zeros of |
Details
Given two i.i.d. samples x_1, \ldots, x_{n_1}
and y_1, \ldots, y_{n_2}
this function computes the
maxima of
D_1(t) = \hat F_{n_1}(t) - \hat F_{n_2}(t)
and
D_2(t) = \hat F^*_{n_1}(t) - \hat F^*_{n_2}(t).
Value
test.stat |
A two-dimensional vector containing the above maxima. |
location |
A two-dimensional vector where the maxima occur. |
Warning
Note that the algorithm that finds the maximal difference for the smoothed estimate is of approximative nature only. It may fail for very large sample sizes.
Author(s)
Kaspar Rufibach, kaspar.rufibach@gmail.com,
http://www.kasparrufibach.ch
Lutz Duembgen, duembgen@stat.unibe.ch,
https://www.imsv.unibe.ch/about_us/staff/prof_dr_duembgen_lutz/index_eng.html
References
Duembgen, L. and Rufibach, K. (2009) Maximum likelihood estimation of a log–concave density and its distribution function: basic properties and uniform consistency. Bernoulli, 15(1), 40–68.
Duembgen, L. and Rufibach, K. (2011) logcondens: Computations Related to Univariate Log-Concave Density Estimation. Journal of Statistical Software, 39(6), 1–28. doi:10.18637/jss.v039.i06
Examples
n1 <- 100
n2 <- 120
x <- sort(rgamma(n1, 2, 1))
y <- sort(rgamma(n2, 2, 1))
res1 <- logConDens(x, smoothed = TRUE)
res2 <- logConDens(y, smoothed = TRUE)
d <- maxDiffCDF(res1, res2, n.grid = 200)
## log-concave estimate
xs <- seq(min(res1$xs, res2$xs), max(res1$xs, res2$xs), length = 200)
F1 <- matrix(NA, nrow = length(xs), ncol = 1); F2 <- F1
for (i in 1:length(xs)){
F1[i] <- evaluateLogConDens(xs[i], res1, which = 3)[, "CDF"]
F2[i] <- evaluateLogConDens(xs[i], res2, which = 3)[, "CDF"]
}
par(mfrow = c(1, 2))
plot(xs, abs(F1 - F2), type = "l")
abline(v = d$location[1], lty = 2, col = 3)
abline(h = d$test.stat[1], lty = 2, col = 3)
## smooth estimate
xs <- seq(min(res1$xs, res2$xs), max(res1$xs, res2$xs), length = 200)
F1smooth <- matrix(NA, nrow = length(xs), ncol = 2); F2smooth <- F1smooth
for (i in 1:length(xs)){
F1smooth[i, ] <- evaluateLogConDens(xs[i], res1, which = 4:5)[,
c("smooth.density", "smooth.CDF")]
F2smooth[i, ] <- evaluateLogConDens(xs[i], res2, which = 4:5)[,
c("smooth.density", "smooth.CDF")]
}
plot(xs, abs(F1smooth[, 2] - F2smooth[, 2]), type = "l")
abline(h = 0)
abline(v = d$location[2], lty = 2, col = c(3, 4))
abline(h = d$test.stat[2], lty = 2, col = c(3, 4))