essHist-package: The Essential Histogram In essHist: The Essential Histogram

Description

Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.

Details

 Package: essHist Type: Package Version: 1.0.1 Date: 2018-01-30 License: The GNU General Public License

Index:

 ```1 2 3 4 5 6 7 8``` ```essHistogram Compute the essential histogram msQuantile Simulate the quantile of multiscale statistics checkHistogram Check any estimator by the multiscale confidence set dmixnorm Compute density function of Gaussian mixtures pmixnorm Compute distribution function of Gaussian mixtures rmixnorm Generate random number of Gaussian mixtures paramExample Output detailed parameters for some famous examples ```

Author(s)

Housen Li [aut, cre], Hannes Sieling [aut]

Maintainer: Housen Li <[email protected]>

References

Li, H., Munk, A., Sieling, H., and Walther, G. (2016). The essential histogram. arXiv:1612.07216

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37``` ```# Simulate data set.seed(123) n = 300 y = rnorm(n) # Compute the essential histogram eh = essHistogram(y, plot = FALSE) # Plot results # compute oracle density x = seq(min(y), max(y), length.out = n) od = dnorm(x) # compare with orcle density plot(x, od, type = "l", xlab = NA, ylab = NA, col = "red") lines(eh) legend("topright", c("Oracle density", "Essential histogram"), lty = c(1,1), col = c("red", "black")) ########################################################### # Evaluate other method e.g. R default histogram function # Data: mixture of Gaussians 1/3 N(0,0.5) + 1/3 N(5,1) + 1/3 N(15,2) set.seed(123) n = 300 y = rmixnorm(n, mean = c(0, 5, 15), sd = c(0.5, 1, 2)) # Oracle density sy = sort(y) ho = dmixnorm(sy, mean = c(0, 5, 15), sd = c(0.5, 1, 2)) # R default histogram h = hist(y, plot = FALSE) # Check R default histogram to local multiscale constriants b = checkHistogram(h, y) lines(sy, ho, col = "red") legend("topright", c("R-Histogram", "Truth"), col = c("black", "red"), lty = c(1,1)) ```

essHist documentation built on April 9, 2018, 5:04 p.m.