bets.inference {bets.covid19} R Documentation

## Likelihood inference

### Description

Likelihood inference

### Usage

```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.

### Details

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

### Value

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

### 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)

```

[Package bets.covid19 version 1.0.0 Index]