Fast implementation of hazard, cumulative hazard, ... for mixtures of subpopulations

Usage

mixture_haz_fun(p, pdfs, survs)

mixture_cumhaz_fun(p, survs)

mixture_cdf_fun(p, cdfs)

mixture_pdf_fun(p, pdfs)

mixture_surv_fun(p, survs)

mixture_quant_fun(p, cdfs, quants)

mixture_rng_fun(p, rngs)

Arguments

p: vector of probabilities of the mixture
pdfs: list of probability density functions of the mixture components
survs: list of survuval functions of the mixture components
cdfs: list of cumulative density functions of the mixture components
quants: list of quantile functions of the mixture components
rngs: random number generating functions of the components

Value

A function with one parameter, a vector of times/probabilities where the function should be evaluated.

Details

the last time interval extends to +Inf

mixture_quant_fun relies on numeric root finding and is therefore not as fast as miniPCH::qpch_fun.

mixture_rng samples the counts from the respective mixtures from a multinomial distribution with parameter p and then samples from the components and shuffles the result.

Functions

mixture_haz_fun(): hazard function of mixture
mixture_cumhaz_fun(): cumulative hazard function of mixture
mixture_cdf_fun(): cumulative density function of mixture
mixture_pdf_fun(): probability density function of mixture
mixture_surv_fun(): survival function of mixture
mixture_quant_fun(): quantile function of mixture
mixture_rng_fun(): quantile function of mixture

Examples

haz <- mixture_haz_fun(
  p = c(0.3, 0.7),
  pdfs = list(
    miniPCH::dpch_fun(0, 0.1),
    miniPCH::dpch_fun(c(0,5), c(0.1, 0.12))
  ),
  survs = list(
    miniPCH::spch_fun(0, 0.1),
    miniPCH::spch_fun(c(0,5), c(0.1, 0.12))
  )
)
plot(haz(seq(0, 30, by=0.15)), ylim=c(0, 0.2), type="l")
abline(h=0)

cumhaz <- mixture_cumhaz_fun(
  p = c(0.3, 0.7),
  survs = list(
    miniPCH::spch_fun(0, 0.1),
    miniPCH::spch_fun(c(0,5), c(0.1, 0.12))
  )
)
plot(cumhaz(seq(0, 30, by=0.15)), type="l")

cdf <- mixture_cdf_fun(
  p = c(0.3, 0.7),
  cdfs = list(
    miniPCH::ppch_fun(0, 0.1),
    miniPCH::ppch_fun(c(0,5), c(0.1, 0.12))
  )
)
plot(cdf(seq(0, 30, by=0.15)), type="l")

pdf <- mixture_pdf_fun(
  p = c(0.3, 0.7),
  pdfs = list(
    miniPCH::dpch_fun(0, 0.1),
    miniPCH::dpch_fun(c(0,5), c(0.1, 0.12))
  )
)
plot(pdf(seq(0, 30, by=0.15)), type="l")

surv <- mixture_surv_fun(
  p = c(0.3, 0.7),
  survs = list(
    miniPCH::spch_fun(0, 0.1),
    miniPCH::spch_fun(c(0,5), c(0.1, 0.12))
  )
)
plot(surv(seq(0, 30, by=0.15)), type="l")


quant <- mixture_quant_fun(
  p = c(0.3, 0.7),
  cdfs = list(
    miniPCH::ppch_fun(0, 0.1),
    miniPCH::ppch_fun(c(0,5), c(0.1, 0.12))
  ),
  quants = list(
    miniPCH::qpch_fun(0, 0.1),
    miniPCH::qpch_fun(c(0,5), c(0.1, 0.12))
  )
)

x <- seq(0, 1, by=0.015)
plot(x, quant(x), type="l")

rng <- mixture_rng_fun(
  p = c(0.3, 0.7),
  rngs = list(
    miniPCH::rpch_fun(0, 0.1, discrete = TRUE),
    miniPCH::rpch_fun(c(0,5), c(0.1, 0.12), discrete = TRUE)
  )
)
hist(rng(100))