bets.inference function

Likelihood inference

bets.inference(
  data,
  likelihood = c("conditional", "unconditional"),
  ci = c("lrt", "point", "bootstrap"),
  M = Inf,
  r = NULL,
  L = NULL,
  level = 0.95,
  bootstrap = 1000,
  mc.cores = 1
)

Arguments

• data: A data.frame with three columns: B, E, S.
• likelihood: Conditional on B and E?
• ci: How to compute the confidence interval?
• M: Right truncation for symptom onset (only available for conditional likelihood)
• r: Parameter for epidemic growth (overrides {params}, only available for conditional likelihood)
• L: Time of travel restriction (required for unconditional likelihood)
• level: Level of the confidence interval (default 0.95).
• bootstrap: Number of bootstrap resamples.
• mc.cores: Number of cores used for computing the bootstrap confidence interval.

Returns

Results of the likelihood inference, including maximum likelihood estimators and individual confidence intervals for the model parameters based on inverting the likelihood ratio test.

Details

The confidence interval is either not computed ("point"), or computed by inverting the likelihood ratio test ("lrt") or basic bootstrap ("bootstrap")

Examples

data(wuhan_exported)

data <- subset(wuhan_exported, Location == "Hefei")
data$B <- data$B - 0.75
data$E <- data$E - 0.25
data$S <- data$S - 0.5

# Conditional likelihood inference
bets.inference(data, "conditional")
bets.inference(data, "conditional", "bootstrap", bootstrap = 100, level = 0.5)

# Unconditional likelihood inference
bets.inference(data, "unconditional", L = 54)

# Conditional likelihood inference for data with right truncation
bets.inference(subset(data, S <= 60), "conditional", M = 60)

# Conditional likelihood inference with r fixed at 0 (not recommended)
bets.inference(data, "conditional", r = 0)