nll_frailty_logscale: Negative log-likelihood function: frailty variables on...

In anovir: Analysis of Virulence

Description

This negative log-likelihood (nll) function is the same as 'nll_frailty', except it assumes the variables to estimate are on a logscale.

Usage

nll_frailty_logscale(
  log.a1 = log.a1,
  log.b1 = log.b1,
  log.a2 = log.a2,
  log.b2 = log.b2,
  log.theta = log.theta,
  data = data,
  time = time,
  censor = censor,
  infected_treatment = infected_treatment,
  d1 = "Weibull",
  d2 = "Weibull",
  d3 = ""
)

Arguments

log.a1, log.b1	location and scale parameters for background mortality, on a logscale
log.a2, log.b2	location and scale parameters for mortality due to infection, on a logscale
log.theta	parameter describing variance of the unobserved variation in virulence, on a logscale
data	name of data frame containing survival data
time	name of data frame column identifying time of event; time > 0
censor	name of data frame column idenifying if event was death (0) or right-censoring (1)
infected_treatment	name of data frame column identifying if data are from an infected (1) or uninfected (0) treatment
d1, d2	names of probability distributions chosen to describe background mortality and mortality due to infection, respectively; both default to the Weibull distribution
d3	name of probability distribution chosen to describe unobserved frailty; choice of 'gamma' or 'inverse Gaussian'

Value

numeric

See Also

nll_frailty

Examples

### Example 1: unobserved variation in virulence with gamma distribution

# step #1: parameterise nll function to be passed to 'mle2'
    m01_prep_function <- function(
      log.a1 = log.a1, log.b1 = log.b1,
      log.a2 = log.a2, log.b2 = log.b2,
      log.theta = log.theta){
        nll_frailty_logscale(
          log.a1 = log.a1, log.b1 = log.b1,
          log.a2 = log.a2, log.b2 = log.b2,
          log.theta = log.theta,
          data = data_lorenz,
          time = t,
          censor = censored,
          infected_treatment = g,
          d1 = "Gumbel", d2 = "Weibull", d3 = "Gamma"
          )}

# step #2: send 'prep_function' to 'mle2' for maximum likelihood estimation
    m01 <- mle2(
      m01_prep_function,
      start = list(
        log.a1 = 3, log.b1 = 1.5, log.a2 = 0.7, log.b2 = -0.7, log.theta = 1
        )
      )

  summary(m01)

  exp(coef(m01))

anovir documentation built on Oct. 24, 2020, 9:08 a.m.