Predict observed data using estimated Rt

Given an object of class poisson_rt produced with estimate_rt(), calculate predicted observed cases for the estimated Rt values. Note: This function is not intended for "new x" or to produce forecasts, but rather to examine how Rt relates to observables.

Usage

# S3 method for class 'cv_poisson_rt'
predict(object, which_lambda = c("lambda.min", "lambda.1se"), ...)

Arguments

object

result of cross validation of type cv_poisson_rt

which_lambda

Select which lambdas from the object to use. If not provided, all Rt's are returned. Note that new lambdas not originally used in the estimation procedure may be provided, but the results will be calculated by linearly interpolating the estimated Rt's.

The strings lambda.min or lambda.1se are allowed to choose either the lambda that minimizes the cross validation score or the largest lambda whose corresponding cross validation score is within 1 standard error of the minimal cross validation score.

...

not used.

Value

A vector or matrix of predicted case counts.

Examples

y <- c(1, rpois(100, dnorm(1:100, 50, 15) * 500 + 1))
cv <- cv_estimate_rt(y, korder = 3, nfold = 3, nsol = 30)
p <- predict(cv)
p <- predict(cv, which_lambda = cv$lambda[1])
p <- predict(cv, which_lambda = "lambda.1se")
p <- predict(cv, which_lambda = NULL)
plot(y)
matlines(p, lty = 2)