drmdel (version 1.1)

cdfDRM: Estimate the CDF of the populations under the DRM

Description

Suppose we have m+1 samples, labeled as 0, 1, ..., m, whose population distributions satisfy the density ratio model (DRM) (see drmdel for the definition of DRM). The cdfDRM function estimates the cumulative distribution functions (CDF), $F_k$, of the populations.

Usage

cdfDRM(k, q=NULL, drmfit, interpolation=TRUE)

Arguments

k

a vector of labels of populations whose CDFs are to be estimated, with k[i] = 0, 1, ..., m.

q

It can be:

(1) a list whose length is the same as the argument "k". The i$\textsuperscript{th}$ component of this list must be a vector of quantiles at which the k[i]$\textsuperscript{th}$ CDF is estimated.

(2) a singl

drmfit

a fitted DRM object (an output from the drmdel function). See drmdel for details.

interpolation

a logical variable specifying whether to linearly interpolate the estimated CDF to make a smooth CDF estimate; The default is TRUE.

Value

• A list whose length is the same as the argument "k". The i$\textsuperscript{th}$ component of the list is a data frame with the following two columns:

  x -- quantiles at which the CDF of population k[i] is estimated.

  cdf_est -- the corresponding estimated CDF values of population k[i].

Examples

# Data generation
set.seed(25)
n_samples <- c(100, 200, 180, 150, 175)  # sample sizes
x0 <- rgamma(n_samples[1], shape=5, rate=1.8)
x1 <- rgamma(n_samples[2], shape=12, rate=1.2)
x2 <- rgamma(n_samples[3], shape=12, rate=1.2)
x3 <- rgamma(n_samples[4], shape=18, rate=5)
x4 <- rgamma(n_samples[5], shape=25, rate=2.6)
x <- c(x0, x1, x2, x3, x4)

# Fit a DRM with the basis function q(x) = (x, log(abs(x))), which
# is the basis function for gamma family. This basis function is
# the built-in basis function 6.
drmfit <- drmdel(x=x, n_samples=n_samples, basis_func=6)

# Estimate the CDF of population 1 at c(0.5, 1.8, 3) and that of
# population 3 at c(1.0, 2.2).
cdf_est1 <- cdfDRM(k=c(1, 3), q=list(c(0.5, 1.8, 3), c(1.0, 2.2)),
                   drmfit=drmfit)
# Show the output.
names(cdf_est1)
cdf_est1$F1
cdf_est1$F3

# Estimate the CDF of population 2 and 4 at the observed data
# points.
cdf_est <- cdfDRM(k=c(2, 4), drmfit=drmfit)
# Show the output.
names(cdf_est)
cdf_est$F2
cdf_est$F4