Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.align = "center",
fig.width = 7,
fig.height = 5
)
## ----message=FALSE------------------------------------------------------------
library(outbreaks) # for data
library(trending) # for trend fitting
library(dplyr) # for data manipulation
# load data
data(covid19_england_nhscalls_2020)
# select 6 weeks of data (from a period when the prevalence was decreasing)
last_date <- as.Date("2020-05-28")
first_date <- last_date - 8*7
pathways_recent <-
covid19_england_nhscalls_2020 %>%
filter(date >= first_date, date <= last_date) %>%
group_by(date, day, weekday) %>%
summarise(count = sum(count), .groups = "drop")
# split data for fitting and prediction
dat <-
pathways_recent %>%
group_by(date <= first_date + 6*7) %>%
group_split()
fitting_data <- dat[[2]]
pred_data <- select(dat[[1]], date, day, weekday)
## -----------------------------------------------------------------------------
(model <- glm_nb_model(count ~ day + weekday))
(fitted_model <- fit(model, fitting_data))
fitted_model %>% get_result()
# default
fitted_model %>%
predict(pred_data) %>%
get_result()
# without uncertainty
fitted_model %>%
predict(pred_data, uncertain = FALSE) %>%
get_result()
# without prediction intervals
fitted_model %>%
predict(pred_data, add_pi = FALSE) %>%
get_result()
# bootstraped prediction intervals
fitted_model %>%
predict(pred_data, simulate_pi = TRUE) %>%
get_result()
## -----------------------------------------------------------------------------
(model2 <- glm_nb_model(count ~ day + nonexistent))
(fitted_model2 <- fit(model2, fitting_data))
get_result(fitted_model2)
get_errors(fitted_model2)
## -----------------------------------------------------------------------------
models <- list(
simple = lm_model(count ~ day),
glm_poisson = glm_model(count ~ day, family = "poisson"),
glm_negbin = glm_nb_model(count ~ day + weekday),
will_error = glm_nb_model(count ~ day + nonexistant)
)
(fitted_tbl <- fit(models, fitting_data))
get_result(fitted_tbl)
## -----------------------------------------------------------------------------
(pred <- predict(fitted_tbl, pred_data))
get_result(pred)
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