cbmod: Fitting models for Secondary Attack Rate with Chain Binomial response

Description

Fitting models for Secondary Attack Rate with Chain Binomial response

Usage

cbmod(
  y,
  s0,
  x = NULL,
  i0 = 1,
  generations = Inf,
  link = "identity",
  optim_method = "BFGS"
)

Value

A list of class cbmod with the following components:

parameters The point estimate of the regression coefficients.
se Standard error of the regression coefficient estimates.
vcov Variance-Covariance matrix of the regression coefficient estimates.
p_values P-values of the null hypothesis that the regression regression coefficient estimate is 0.
loglikelihood the log likelihood value at the point estimate.
npar Number of parameters.
sar_hat Vector of fitted secondary attack rates.
fitted_values Vector of expected outbreak size (final attack rate).
link Link function used by the regression model.
null_model = Null model, fitted with estimate_sar(). This is equivalent to an intercept only model.
warnings Warning_messages,
est_time: Time used to fit the model.
omitted_values Vector indicating data points that were ignored during estimation because of missing values.

Arguments

y: numeric, the number of infected cases.
s0: numeric, the number of initial susceptibles.
x: matrix of predictors (design matrix).
i0: numeric, number of initial infected. Default is 1.
generations: numeric.
link: Link function. Default is 'identity'.
optim_method: Optimization method used by optim.

Details

The following link functions are available: identity, log, logit, and cloglog.

Examples

Run this code

set.seed(234)
mydata <- data.frame(infected = rchainbinom(n = 15, s0 = 5, sar = 0.2,
   i0 = 1, generations = Inf),
   s0 = 5, i0 = 1, generations = Inf)
xmat <- model.matrix(~ 1, data = mydata)
res <- cbmod(y = mydata$infected, s0 = mydata$s0, x = xmat, i0 = mydata$i0,
   generations = mydata$generations)
summary(res)