Description Usage Arguments Details Value Examples
View source: R/likelihood_inference.R
Likelihood inference
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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 |
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. |
The confidence interval is either not computed ("point"
), or computed by inverting the likelihood ratio test ("lrt"
) or basic bootstrap ("bootstrap"
)
Results of the likelihood inference, including maximum likelihood estimators and individual confidence intervals for the model parameters based on inverting the likelihood ratio test.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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)
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