rlogcon {logcondens} | R Documentation |
Generate a random sample from a distribution with density \hat f_n and \hat f_n^*, as described in Duembgen and Rufibach (2009, Section 3).
rlogcon(n, x0)
n |
Size of random sample to be generated. |
x0 |
Sorted vector of independent and identically distributed numbers. Note that unlike for activeSetLogCon ,
here x0 is the vector of initial, unweighted observations, i.e. identical values are allowed. |
X |
Random sample from \hat f_n. |
X_star |
Random sample from \hat f_n^*. |
U |
Uniform random sample of size n used in the generation of X . |
Z |
Normal random sample of size n used in the generation of X_star . |
f |
Computed log-concave density estimator. |
f.smoothed |
List containing smoothed log-concave density estimator, as output by logconSmoothed . |
x |
Vector of distinct observations generated from x0 . |
w |
Weights corresponding to x . |
Kaspar Rufibach, kaspar.rufibach@ifspm.uzh.ch,
http://www.biostat.uzh.ch/aboutus/people/rufibach.html
Lutz Duembgen, duembgen@stat.unibe.ch,
http://www.staff.unibe.ch/duembgen
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.
## =================================================== ## Generate random samples as described in Section 3 of ## Duembgen and Rufibach (2009) ## =================================================== x0 <- sort(rnorm(111)) n <- 22 random <- rlogcon(n, x0) ## sample of size n from the log-concave density estimator random$X ## sample of size n from the smoothed log-concave density estimator random$X_star