nll_frailty_logscale {anovir}

Negative log-likelihood function: frailty variables on logscale

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))