flatline: (Internal) implementation of the flatline forecaster

In cmu-delphi/epipredict: Basic epidemiology forecasting methods

(Internal) implementation of the flatline forecaster

Description

This is an internal function that is used to create a parsnip::linear_reg() model. It has somewhat odd behaviour (see below).

Usage

flatline(formula, data)

Arguments

formula

The lhs should be a single variable. In standard usage, this would actually be the observed time series shifted forward by the forecast horizon. The right hand side must contain any keys (locations) for the panel data separated by plus. The observed time series must come last. For example form <- as.formula(lead7_y ~ state + age + y) Note that this function doesn't DO the shifting, that has to be done outside.

data

A data frame containing at least the variables used in the formula. It must also contain a column time_value giving the observed time points.

Value

An S3 object of class flatline with two components:

• residuals - a tibble with all keys and a .resid column that contains forecast errors.

• .pred - a tibble with all keys and a .pred column containing only predictions for future data (the last observed of the outcome for each combination of keys.

Examples

tib <- data.frame(
  y = runif(100),
  expand.grid(k = letters[1:4], j = letters[5:9], time_value = 1:5)
) %>%
  dplyr::group_by(k, j) %>%
  dplyr::mutate(y2 = dplyr::lead(y, 2)) # predict 2 steps ahead
flat <- flatline(y2 ~ j + k + y, tib) # predictions for 20 locations
sum(!is.na(flat$residuals$.resid)) # 100 residuals, but 40 are NA

cmu-delphi/epipredict documentation built on March 5, 2025, 12:17 p.m.