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Plot Serial Interval Fit from si_estim Result

Source: R/plot_si_fit.R
plot_si_fit_result.Rd

A convenience wrapper for plot_si_fit that accepts the output from si_estim directly, automatically handling the weight aggregation for different distribution types.

Usage

plot_si_fit_result(
  si_result,
  dat,
  dist = c("normal", "gamma"),
  scaling_factor = 1
)

Arguments

si_result

list; the output from si_estim containing mean, sd, and wts (weights) components

dat

numeric vector; the index case-to-case (ICC) intervals in days used for estimation

dist

character; the distribution family used for estimation. Must be either "normal" (default) or "gamma". Should match the distribution used in the original si_estim() call

scaling_factor

numeric; multiplicative factor to adjust the height of the fitted density curve. Defaults to 1

Value

A ggplot2 object showing the fitted distribution overlaid on a histogram of the observed data

Details

This function simplifies the plotting workflow by automatically aggregating the component weights from si_estim() output:

  • For normal distribution: Aggregates 7 weights into 4 transmission route weights (co-primary, primary-secondary, primary-tertiary, primary-quaternary)

  • For gamma distribution: Passes the first 3 weights (co-primary, primary-secondary, primary-tertiary); the primary-quaternary weight is derived internally by f_gam() as 1 - w1 - w2 - w3

See also

si_estim for serial interval estimation, plot_si_fit for the underlying plotting function

Examples

# Simulate some ICC interval data
set.seed(123)
icc_data <- c(
  abs(rnorm(15, mean = 0, sd = 2)),
  rnorm(40, mean = 12, sd = 3),
  rnorm(15, mean = 24, sd = 4)
)
icc_data <- round(pmax(icc_data, 0))

# Estimate serial interval
# \donttest{
result <- si_estim(icc_data, n = 50)

# Plot using the convenience wrapper
plot_si_fit_result(result, icc_data, dist = "normal")

# }