glm

DESCRIPTION

@@ -22,7 +22,7 @@ Authors@R:
              comment = c(ORCID = "0000-0001-9140-9028"))
       )
 Description: Miscellaneous functions for retrieving data, creating and evaluating time series 
-    forecasting models for influenza-like illness (ILI) cases, deaths, and hospitalizations in 
+    forecasting models for influenza-like illness (ILI) and influenza hospitalizations in 
     the United States.
 License: GPL (>= 3)
 Encoding: UTF-8
@@ -39,6 +39,11 @@ Imports:
     RSocrata,
     tibble,
     tidyr,
-    tsibble
+    tsibble,
+    trending,
+    trendeval,
+    ggplot2,
+    evalcast,
+    yardstick
 Depends: 
     R (>= 2.10)

NAMESPACE

@@ -5,7 +5,13 @@ export(get_cdc_hosp)
 export(get_cdc_ili)
 export(get_cdc_vax)
 export(get_hdgov_hosp)
+export(glm_fit)
+export(glm_forecast)
+export(glm_quibble)
+export(glm_wrap)
 export(is_monday)
 export(make_tsibble)
+export(plot_forc)
 export(this_monday)
+export(wis_score)
 importFrom(magrittr,"%>%")

R/fiphde.R

@@ -48,5 +48,20 @@ if(getRversion() >= "2.15.1")  utils::globalVariables(c(".",
                                                         "sea_label",
                                                         "monday",
                                                         "yweek",
+                                                        "0.025",
+                                                        "0.975",
+                                                        "<NA>",
+                                                        "error",
+                                                        "estimate",
+                                                        "model",
+                                                        "lower",
+                                                        "upper",
+                                                        "lower_pi",
+                                                        "upper_pi",
+                                                        "flu.admits",
+                                                        "quantile",
+                                                        "truth",
+                                                        "rmse",
+                                                        "value",
                                                         "."))
 

R/glm.R

@@ -0,0 +1,208 @@
+#' Fit glm models
+#'
+#' This helper function is used in \link[fiphde]{glm_wrap} to fit a list of models and select the best one.
+#'
+#' @param .data Data including all explanatory and outcome variables needed for modeling; must include column for "location"
+#' @param .models List of models defined as \link[trending]{trending_model} objects
+#'
+#' @return A `tibble` containing characteristics from the "best" `glm` model (i.e., the model from ".models" list with lowest RMSE). The columns in this `tibble` include:
+#'
+#'- model_class: The "type" of model for the best fit
+#'- fit: The fitted model object for the best fit
+#'- location: The geographic
+#'- data: Original model fit data as a `tibble` in a list column
+#'
+#' @export
+#'
+#' @md
+#'
+glm_fit <- function(.data,
+                    .models) {
+
+  dat <- .data
+
+  message(paste0("Location ... "))
+  message(unique(dat$location))
+
+  ## evaluate models and use metrics provided in arg
+  res <-
+    trendeval::evaluate_models(
+      .models,
+      dat,
+      method = trendeval::evaluate_resampling,
+      metrics = list(yardstick::rmse)
+    )
+
+  ## pull the best model by rmse
+  best_by_rmse <-
+    res %>%
+    # dplyr::filter(purrr::map_lgl(warning, is.null)) %>%  # remove models that gave warnings
+    dplyr::filter(purrr::map_lgl(error, is.null))  %>%   # remove models that errored
+    dplyr::slice_min(rmse) %>%
+    dplyr::select(model) %>%
+    purrr::pluck(1,1)
+
+  ## fit the model
+  tmp_fit <-
+    best_by_rmse %>%
+    trending::fit(dat)
+
+  ## construct tibble with model type, actual fit, and the location
+  ret <- dplyr::tibble(model_class = best_by_rmse$model_class,
+                       fit = tmp_fit,
+                       location = unique(dat$location),
+                       data = tidyr::nest(dat, fit_data = dplyr::everything()))
+
+  message("Selected model ...")
+  message(as.character(ret$fit$fitted_model$family)[1])
+
+  message("Variables ...")
+  message(paste0(names(ret$fit$fitted_model$coefficients), collapse = " + "))
+  return(ret)
+
+}
+
+
+#' Get quantiles from prediction intervals
+#'
+#' This function runs the \link[trending]{predict.trending_model_fit} method on a fitted model at specified values of "alpha" in order to create a range of prediction intervals. The processing also includes steps to convert the alpha to corresponding quantile values at upper and lower bounds.
+#'
+#' @param fit Fitted model object from \link[fiphde]{glm_fit}
+#' @param new_data Tibble with new data on which the \link[trending]{predict.trending_model_fit} method should run
+#' @param alpha Vector specifying the threshold(s) to be used for prediction intervals; alpha of `0.05` would correspond to 95% PI; default is `c(0.01, 0.025, seq(0.05, 0.45, by = 0.05)) * 2` to range of intervals
+#'
+#' @return A tibble with predicted values at each quantile (lower and upper bound for each value of "alpha")
+#' @export
+#' @md
+#'
+glm_quibble <- function(fit, new_data, alpha = c(0.01, 0.025, seq(0.05, 0.45, by = 0.05)) * 2) {
+
+  ## get the quantiles from the alpha
+  q_lower <- alpha/2
+  q_upper <- 1 - q_lower
+
+  ## run the predict method on the fitted model
+  ## use the given alpha
+  fit %>%
+    stats::predict(new_data, alpha = alpha) %>%
+    ## get just the prediction interval bounds ...
+    ## index (time column must be named index) ...
+    dplyr::select(epiyear, epiweek, lower_pi, upper_pi) %>%
+    ## reshape so that its in long format
+    tidyr::gather(quantile, value, lower_pi:upper_pi) %>%
+    ## and subout out lower_pi/upper_pi for the appropriate quantile
+    dplyr::mutate(quantile = ifelse(quantile == "lower_pi", q_lower, q_upper))
+}
+
+
+#' Forecast glm models
+#'
+#' This function uses fitted model object from \link[fiphde]{glm_fit} and future covariate data to create probablistic forecasts at specific quantiles derived from the "alpha" parameter.
+#'
+#' @param .data Data including all explanatory and outcome variables needed for modeling
+#' @param new_covariates Tibble with one column per covariate, and n rows for n horizons being forecasted
+#' @param fit Fitted model object from \link[fiphde]{glm_fit}
+#' @param alpha Vector specifying the threshold(s) to be used for prediction intervals; alpha of `0.05` would correspond to 95% PI; default is `c(0.01, 0.025, seq(0.05, 0.45, by = 0.05)) * 2` to range of intervals
+#'
+#' @return Tibble with forecasts (quantiles and point estimates)
+#' @export
+#' @md
+#'
+glm_forecast <- function(.data, new_covariates = NULL, fit, alpha = c(0.01, 0.025, seq(0.05, 0.45, by = 0.05)) * 2) {
+
+  ## get the last date from the data provided
+  last_date <-
+    .data %>%
+    dplyr::arrange(location, epiweek, epiyear) %>%
+    dplyr::mutate(date = MMWRweek::MMWRweek2Date(epiyear, epiweek)) %>%
+    dplyr::pull(date) %>%
+    utils::tail(1)
+
+  tmp <- .data
+  ## set up "new data" with the epiweek/epiyear week being forecasted  ...
+  ## and calculation of lagged flu admissions (1 to 4 weeks back)
+  new_data <-
+    dplyr::tibble(lag_1 = utils::tail(tmp$flu.admits, 4)[4],
+                  lag_2 = utils::tail(tmp$flu.admits, 4)[3],
+                  lag_3 = utils::tail(tmp$flu.admits, 4)[2],
+                  lag_4 = utils::tail(tmp$flu.admits, 4)[1],
+                  epiweek = lubridate::epiweek(last_date + 7),
+                  epiyear = lubridate::epiyear(last_date + 7))
+
+  ## this should allow for a constant
+  if(!is.null(new_covariates)) {
+    new_data <-
+      cbind(new_data,new_covariates)
+  }
+  # ## take the fit object provided and use predict
+  point_estimates <-
+    fit %>%
+    stats::predict(new_data) %>%
+    dplyr::select(epiweek, epiyear, estimate) %>%
+    dplyr::mutate(estimate = round(estimate)) %>%
+    dplyr::mutate(quantile = NA) %>%
+    dplyr::select(epiweek, epiyear, quantile, value = estimate)
+
+  ## map the quibble function over the alphas
+  quants <- purrr::map_df(alpha, .f = function(x) glm_quibble(fit = fit, new_data = new_data, alpha = x))
+
+  ## prep data
+  dplyr::bind_rows(point_estimates,quants) %>%
+    dplyr::arrange(epiyear,epiweek, quantile) %>%
+    dplyr::left_join(new_data, by = c("epiweek","epiyear")) %>%
+    dplyr::select(epiweek,epiyear,quantile,value)
+}
+
+#' Run glm modeling and forecasting
+#'
+#' This is a wrapper function that pipelines influenza hospitalization modeling (\link[fiphde]{glm_fit}) and forecasting (\link[fiphde]{glm_forecast}).
+#'
+#' @param .data Data including all explanatory and outcome variables needed for modeling
+#' @param .models List of models defined as \link[trending]{trending_model} objects
+#' @param new_covariates Tibble with one column per covariate, and n rows for n horizons being forecasted
+#' @param horizon Number of weeks ahead for forecasting
+#' @param alpha Vector specifying the threshold(s) to be used for prediction intervals; alpha of `0.05` would correspond to 95% PI; default is `c(0.01, 0.025, seq(0.05, 0.45, by = 0.05)) * 2` to range of intervals
+#'
+#' @return Named list with two elements:
+#'
+#' - model: Output from \link[fiphde]{glm_fit} with selected model fit
+#' - forecasts: Output from \link[fiphde]{glm_forecast} with forecasts from each horizon combined as a single tibble
+#'
+#' @export
+#' @md
+glm_wrap <- function(.data, .models, new_covariates = NULL, horizon = 4, alpha = c(0.01, 0.025, seq(0.05, 0.45, by = 0.05)) * 2) {
+
+  tmp_fit <- glm_fit(.data, .models = .models)
+
+  stopifnot(nrow(new_covariates) == horizon)
+  message("Forecasting 1 week ahead")
+  tmp_forc <-
+    glm_forecast(.data = .data, new_covariates = new_covariates[1,], fit = tmp_fit$fit, alpha = alpha)
+
+  forc_list <- list()
+  forc_list[[1]] <- tmp_forc
+
+  if(horizon > 1) {
+
+    for(i in 2:horizon) {
+
+      message(sprintf("Forecasting %d week ahead",i))
+
+      prev_weeks <-
+        do.call("rbind", forc_list)  %>%
+        ## get point estimate ... which will have NA quantile value
+        dplyr::filter(is.na(quantile)) %>%
+        dplyr::rename(flu.admits = value) %>%
+        dplyr::select(-quantile) %>%
+        dplyr::mutate(flu.admits.cov = NA, location = "US")
+
+      forc_list[[i]] <- glm_forecast(.data = dplyr::bind_rows(.data, prev_weeks),
+                                     new_covariates = new_covariates[i,],
+                                     fit = tmp_fit$fit,
+                                     alpha = alpha)
+
+    }
+  }
+  forc_res <- do.call("rbind", forc_list)
+  return(list(model = tmp_fit, forecasts = forc_res))
+}

R/utils.R

@@ -55,3 +55,61 @@ this_monday <- function() {
 is_monday <- function() {
   lubridate::wday(lubridate::today(), label=TRUE) %in% c("Mon")
 }
+
+#' Calculate WIS score
+#'
+#' Helper function to calculate weighted interval score (WIS) for prepped forecasts
+#'
+#' @param .forecasts Tibble with prepped foreacsts
+#' @param .test Tibble with test data including observed value for flu admissions stored in "flu.admits" column
+#'
+#' @return Tibble with the WIS for each combination of epiweek and epiyear
+#' @export
+#'
+wis_score <- function(.forecasts, .test) {
+  .forecasts %>%
+    dplyr::left_join(.test) %>%
+    dplyr::select(epiweek,epiyear,quantile,value,flu.admits) %>%
+    dplyr::group_by(epiweek, epiyear) %>%
+    dplyr::summarise(wis = evalcast::weighted_interval_score(quantile = quantile, value = value, actual_value = flu.admits))
+}
+
+#' Plot forecasts against observed data
+#'
+#' This helper function creates a plot to visualize the forecasted point estimates (and 95% prediction interval) alongside the observed data.
+#'
+#' @param .forecasts Tibble with prepped forecasts
+#' @param .train Tibble with data used for modeling
+#' @param .test Tibble with observed data held out from modeling
+#'
+#' @return A `ggplot2` plot object
+#' @export
+#'
+#'
+plot_forc <- function(.forecasts, .train, .test) {
+
+  forc_dat <-
+    .forecasts %>%
+    dplyr::filter(quantile %in% c(NA,0.025,0.975)) %>%
+    tidyr::spread(quantile,value) %>%
+    dplyr::rename(lower = `0.025`, upper = `0.975`, mean = `<NA>`)
+
+  .test %>%
+    dplyr::bind_rows(.train) %>%
+    dplyr::select(epiweek,epiyear, truth = flu.admits, location) %>%
+    dplyr::left_join(forc_dat) %>%
+    dplyr::mutate(date = MMWRweek::MMWRweek2Date(epiyear, epiweek)) %>%
+    ggplot2::ggplot() +
+    ggplot2::geom_line(ggplot2::aes(date,truth), lwd = 2, col = "black") +
+    ggplot2::geom_line(ggplot2::aes(date,mean), lwd = 2, alpha = 0.5, lty = "solid", col = "firebrick") +
+    ggplot2::geom_ribbon(ggplot2::aes(date, ymin = lower, ymax = upper), alpha = 0.25, fill = "firebrick") +
+    ## get an upper limit from whatever the max of observed or forcasted hospitalizations is
+    ggplot2::scale_y_continuous(limits = c(0,max(c(.test$flu.admits, .train$flu.admits, forc_dat$upper)))) +
+    ggplot2::scale_x_date(date_labels = "%Y-%m", date_breaks = "month") +
+    ggplot2::labs(x = "Date", y = "Count", title = "Influenza hospitalizations") +
+    ggplot2::theme_minimal() +
+    ggplot2::facet_wrap(~ location)
+
+}
+
+