nll_recovery {anovir}

Negative log-likelihood function: recovery model

Description

Function returning negative log-likelihood (nll) for patterns of survival in infected and uninfected treatments, when infected hosts can recover from infection.

Usage

nll_recovery(
  a1 = a1,
  b1 = b1,
  a2 = a2,
  b2 = b2,
  a3 = a3,
  b3 = b3,
  data = data,
  d1 = "",
  d2 = "",
  d3 = ""
)

Arguments

a1, b1

location & scale parameters for background mortality

a2, b2

location & scale parameters for mortality due to infection

a3, b3

location & scale parameters for how long infection 'survives'

data

a data.frame with the data

d1, d2, d3

probability distributions for background mortality, mortality due to infection & how long infection 'survives' ("Weibull", "Gumbel", "Frechet")

Details

This model assumes all the hosts in an infected treatment are all initially infected, and they can all potentially recover from infection. Recovered hosts are assumed to only experience background mortality equivalent to that experienced by matching uninfected or control individuals; no assumptions are made as to whether they are still infected or infectious. It is also assumed that the timing of recovery from infection is not directly observed, but an individual's infected/recovery status can be determined after they have died or been censored.

The probability that an infection 'survives' over time, i.e., the host does not recover from infection, is assumed to follow a probability distribution which acts independently of the probability distributions determining background mortality or mortality due to infection.

This function only estimates location and scale parameters as constants, it is not designed to estimate them as functions.

Value

numeric

Warning

requires the data to be specified in a specific format; see vignette 'data format' for details

Examples

# NB the data to analyse needs to be in a data frame of a specific form
  head(recovery_data)

# step #1: prepare nll function for analysis
  m01_prep_function <- function(a1, b1, a2, b2, a3, b3){
    nll_recovery(a1, b1, a2, b2, a3, b3,
                 data = recovery_data,
                 d1 = "Weibull", d2 = "Weibull", d3 = "Weibull"
                 )}

# step #2: send 'prep_function' to mle2 for maximum likelihood estimation,
  # specifying starting values
  m01 <- mle2(m01_prep_function,
    start = list(a1 = 2, b1 = 0.5, a2 = 2, b2 = 0.5, a3 = 2, b3 = 0.5)
    )

  summary(m01)

# values used to simulate data were for the Weibull distribution;
  # a1 = 2.8, b1 = 0.5, a2 = 2.2, b2 = 0.35, a3 = 2.35, b3 = 0.35