nll_frailty_logscale function

Negative log-likelihood function: frailty variables on logscale

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

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'

Returns

numeric

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))

See Also

nll_frailty