SimNPH (version 0.5.7)

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

Description

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)

Value

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

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

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

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.

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))