Introduce construct.model parameter

construct.model is a function that takes modeling data (NA in
post.period) and returns a bsts model.

This invocation:

    go <- function(data) {
      y <- data[, 1]
      sdy <- sd(y, na.rm = TRUE)
      sd.prior <- SdPrior(sigma.guess = 0.01 * sdy,
                          upper.limit = sdy,
                          sample.size = 32)
      ss <- AddLocalLevel(list(), y, sigma.prior = sd.prior)
      bsts.model <- bsts(y, state.specification = ss, niter = 100,
                         seed = 1, ping = 0)
    }

    impact <- CausalImpact(data, pre.period, post.period,
                           construct.model = go)

is equivalent to:

    model.args <- list(niter = 100)
    impact <- CausalImpact(data, pre.period, post.period, model.args)

. This change provides an interface with RunWithData's flexibility with
pre- and post-period, with RunWithBstsModel's full configurability of
the model. For example, the caller can now specify a custom model where
the post-period is before the end of the data, or with a gap between the
pre-period and post-period, both of which aren't possible with the
previous interface.

R/impact_analysis.R

@@ -135,7 +135,8 @@ FormatInputPrePostPeriod <- function(pre.period, post.period, data) {
 
 FormatInputForCausalImpact <- function(data, pre.period, post.period,
                                        model.args, bsts.model,
-                                       post.period.response, alpha) {
+                                       post.period.response, alpha,
+                                       construct.model) {
   # Checks and formats all input arguments supplied to CausalImpact(). See the
   # documentation of CausalImpact() for details.
   #
@@ -147,6 +148,7 @@ FormatInputForCausalImpact <- function(data, pre.period, post.period,
   #   bsts.model:           fitted bsts model (instead of data)
   #   post.period.response: observed response in the post-period
   #   alpha:                tail-area for posterior intervals
+  #   construct.model:      custom model constructor
   #
   # Returns:
   #   list of checked (and possibly reformatted) input arguments
@@ -213,7 +215,8 @@ CausalImpact <- function(data = NULL,
                          model.args = NULL,
                          bsts.model = NULL,
                          post.period.response = NULL,
-                         alpha = 0.05) {
+                         alpha = 0.05,
+                         construct.model = NULL) {
   # CausalImpact() performs causal inference through counterfactual
   # predictions using a Bayesian structural time-series model.
   #
@@ -278,6 +281,8 @@ CausalImpact <- function(data = NULL,
   #   alpha:       Desired tail-area probability for posterior intervals.
   #                Defaults to 0.05, which will produce central 95\% intervals.
   #
+  #   construct.model: Custom model constructor.
+  #
   # Returns:
   #   A CausalImpact object. This is a list of:
   #     series:  observed data, counterfactual, pointwise and cumulative impact
@@ -341,7 +346,8 @@ CausalImpact <- function(data = NULL,
   # Check input
   checked <- FormatInputForCausalImpact(data, pre.period, post.period,
                                         model.args, bsts.model,
-                                        post.period.response, alpha)
+                                        post.period.response, alpha,
+                                        construct.model)
   data <- checked$data
   pre.period <- checked$pre.period
   post.period <- checked$post.period
@@ -352,7 +358,7 @@ CausalImpact <- function(data = NULL,
 
   # Depending on input, dispatch to the appropriate Run* method()
   if (!is.null(data)) {
-    impact <- RunWithData(data, pre.period, post.period, model.args, alpha)
+    impact <- RunWithData(data, pre.period, post.period, model.args, alpha, construct.model)
     # Return pre- and post-period in the time unit of the time series.
     times <- time(data)
     impact$model$pre.period <- times[pre.period]
@@ -364,7 +370,7 @@ CausalImpact <- function(data = NULL,
   return(impact)
 }
 
-RunWithData <- function(data, pre.period, post.period, model.args, alpha) {
+RunWithData <- function(data, pre.period, post.period, model.args, alpha, construct.model) {
   # Runs an impact analysis on top of a fitted bsts model.
   #
   # Args:
@@ -375,6 +381,7 @@ RunWithData <- function(data, pre.period, post.period, model.args, alpha) {
   #                limits.
   #   model.args:  list of model arguments
   #   alpha:       tail-probabilities of posterior intervals
+  #   construct.model: custom model constructor
   #
   # Returns:
   #   See CausalImpact().
@@ -409,7 +416,16 @@ RunWithData <- function(data, pre.period, post.period, model.args, alpha) {
   window(data.modeling[, 1], start = pre.period[2] + 1) <- NA
 
   # Construct model and perform inference
-  bsts.model <- ConstructModel(data.modeling, model.args)
+  if (!is.null(construct.model)) {
+    checked <- FormatInputForConstructModel(data.modeling, model.args)
+    y <- checked$data[, 1]
+    # If the series is ill-conditioned, abort inference and return NULL
+    bsts.model <- if (ObservationsAreIllConditioned(y)) NULL else {
+      construct.model(checked$data)
+    }
+  } else {
+    bsts.model <- ConstructModel(data.modeling, model.args)
+  }
 
   # Compile posterior inferences
   if (!is.null(bsts.model)) {

tests/testthat/test-impact-analysis.R

@@ -593,6 +593,33 @@ test_that("CausalImpact.RunWithData.MissingTimePoint", {
   expect_equal(indices, time(series)[-17])
 })
 
+test_that("CausalImpact.RunWithData.CustomConstructModel", {
+  # Test daily data (zoo object)
+  data <- zoo(cbind(rnorm(200), rnorm(200), rnorm(200)),
+              seq.Date(as.Date("2014-01-01"), as.Date("2014-01-01") + 199,
+                       by = 1))
+
+  pre.period <- as.Date(c("2014-01-01", "2014-04-10"))  # 100 days
+  post.period <- as.Date(c("2014-04-21", "2014-07-09"))  # 80 days
+
+  go <- function(data) {
+    y <- data[, 1]
+    sdy <- sd(y, na.rm = TRUE)
+    sd.prior <- SdPrior(sigma.guess = 0.01 * sdy,
+                        upper.limit = sdy,
+                        sample.size = 32)
+    ss <- AddLocalLevel(list(), y, sigma.prior = sd.prior)
+    bsts.model <- bsts(y, state.specification = ss, niter = 100,
+                       seed = 1, ping = 0)
+  }
+
+  suppressWarnings(impact <- CausalImpact(data, pre.period, post.period,
+                                          construct.model = go))
+  expect_equal(time(impact$model$bsts.model$original.series), 1:200)
+  expect_equal(time(impact$series), time(data))
+  CallAllS3Methods(impact)
+})
+
 test_that("CausalImpact.RunWithBstsModel", {
 
   # Test on a healthy bsts object