cbmod {chainbinomial}

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"
)

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.

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.

See Also

Methods for cbmod objects:

• summary.cbmod()

• predict.cbmod()

• coef.cbmod()

• confint.cbmod()

• vcov.cbmod()

• tidy.cbmod()

• glance.cbmod()

Examples

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)