nll_exposed_infected: Negative log-likelihood function: exposed-infected

Description

Function returning negative log-likelihood (nll) for patterns of mortality in infected and control treatments, where the infected population harbours an unobserved proportion of hosts that were exposed to infection, but did not become infected.

Usage

nll_exposed_infected(
  a1 = a1,
  b1 = b1,
  a2 = a2,
  b2 = b2,
  p1 = p1,
  data = data,
  time = time,
  censor = censor,
  infected_treatment = infected_treatment,
  d1 = "Weibull",
  d2 = "Weibull"
)

Arguments

a1, b1

location and scale parameters for background mortality

a2, b2

location and scale parameters for mortality due to infection

unobserved proportion of hosts exposed to infection and infected; 0 <= p1 <= 1

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

Value

numeric

Details

This function returns the nll based on five parameters, the location and scale parameters for background mortality and mortality due to infection, respectively, plus a parameter for the proportion of hosts that became infected when exposed to infection.

Examples

Run this code

# NOT RUN {
# check column names in head of data frame with data to analyse
    head(data_parker)

# step #1: prepare nll function for analysis
    m01_prep_function <- function(a1 = a1, b1 = b1, a2 = a2, b2 = b2, p1 = p1){
      nll_exposed_infected(
        a1 = a1, b1 = b1, a2 = a2, b2 = b2, p1 = p1,
        data = data_parker,
        time = t,
        censor = censored,
        infected_treatment = g,
        d1 = "Frechet",
        d2 = "Weibull")
        }

# step #2: send 'prep_function' to mle2 for maximum likelihood estimation
    m01 <- mle2(m01_prep_function,
             start = list(a1 = 2.5, b1 = 1, a2 = 2, b2 = 0.5, p1 = 0.5)
             )

    summary(m01)

# model setting lower & upper bounds to parameter estimates
  # including 0 < p1 < 1
    m02 <- mle2(m01_prep_function,
             start = list(a1 = 2.5, b1 = 1.2, a2 = 1.9, b2 = 0.16, p1 = 0.48),
             method = "L-BFGS-B",
             lower = c(a1 = 0, b1 = 0, a2 = 0, b2 = 0, p1 = 0),
             upper = c(a1 = Inf, b1 = Inf, a2 = Inf, b2 = Inf, p1 = 1),
             )

    summary(m02)


# }