Whitespace and documentation fixes

R/NAMESPACE

@@ -1,9 +1,9 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(plot,prophet)
 S3method(predict,prophet)
 export(fit.prophet)
 export(make_future_dataframe)
-export(plot.prophet)
 export(predictive_samples)
 export(prophet)
 export(prophet_plot_components)

R/R/prophet.R

@@ -780,12 +780,12 @@ sample_posterior_predictive <- function(m, df) {
   n.iterations <- length(m$params$k)
   samp.per.iter <- max(1, ceiling(m$uncertainty.samples / n.iterations))
   nsamp <- n.iterations * samp.per.iter  # The actual number of samples
-  
+
   seasonal.features <- make_all_seasonality_features(m, df)
   sim.values <- list("trend" = matrix(, nrow = nrow(df), ncol = nsamp),
                      "seasonal" = matrix(, nrow = nrow(df), ncol = nsamp),
                      "yhat" = matrix(, nrow = nrow(df), ncol = nsamp))
-  
+
   for (i in 1:n.iterations) {
     # For each set of parameters from MCMC (or just 1 set for MAP),
     for (j in 1:samp.per.iter) {
@@ -799,7 +799,23 @@ sample_posterior_predictive <- function(m, df) {
   }
   return(sim.values)
 }  
-  
+
+#' Sample from the posterior predictive distribution.
+#'
+#' @param m Prophet object.
+#' @param df Dataframe with dates for predictions (column ds), and capacity
+#'  (column cap) if logistic growth.
+#'
+#' @return A list with items "trend", "seasonal", and "yhat" containing
+#'  posterior predictive samples for that component.
+#'
+#' @export
+predictive_samples <- function(m, df) {
+    df <- setup_dataframe(m, df)$df
+    sim.values <- sample_posterior_predictive(m, df)
+    return(sim.values)
+}
+
 #' Prophet uncertainty intervals.
 #'
 #' @param m Prophet object.
@@ -812,7 +828,7 @@ predict_uncertainty <- function(m, df) {
   # Add uncertainty estimates
   lower.p <- (1 - m$interval.width)/2
   upper.p <- (1 + m$interval.width)/2
-  
+
   intervals <- cbind(
     t(apply(t(sim.values$yhat), 2, stats::quantile, c(lower.p, upper.p),
             na.rm = TRUE)),
@@ -821,13 +837,12 @@ predict_uncertainty <- function(m, df) {
     t(apply(t(sim.values$seasonal), 2, stats::quantile, c(lower.p, upper.p),
             na.rm = TRUE))
   ) %>% dplyr::as_data_frame()
-  
+
   colnames(intervals) <- paste(rep(c('yhat', 'trend', 'seasonal'), each=2),
                                c('lower', 'upper'), sep = "_")
   return(intervals)
 }
 
-
 #' Simulate observations from the extrapolated generative model.
 #'
 #' @param m Prophet object.
@@ -1175,20 +1190,4 @@ plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
   return(gg.yearly)
 }
 
-#' Sample from the posterior predictive distribution.
-#'
-#' @param m Prophet object.
-#' @param df Dataframe with dates for predictions (column ds), and capacity
-#'  (column cap) if logistic growth.
-#'
-#' @return A list with items "trend", "seasonal", and "yhat" containing 
-#'  posterior predictive samples for that component.
-#'
-#' @export
-predictive_samples <- function(m, df) {
-    df <- setup_dataframe(m, df)$df
-    sim.values <- sample_posterior_predictive(m, df)
-    return(sim.values)
-}
-
-# fb-block 3
+# fb-block 3

R/man/plot.prophet.Rd

@@ -4,8 +4,8 @@
 \alias{plot.prophet}
 \title{Plot the prophet forecast.}
 \usage{
-plot.prophet(x, fcst, uncertainty = TRUE, plot_cap = TRUE, xlabel = "ds",
-  ylabel = "y", ...)
+\method{plot}{prophet}(x, fcst, uncertainty = TRUE, plot_cap = TRUE,
+  xlabel = "ds", ylabel = "y", ...)
 }
 \arguments{
 \item{x}{Prophet object.}

R/man/predictive_samples.Rd

@@ -13,7 +13,7 @@ predictive_samples(m, df)
 (column cap) if logistic growth.}
 }
 \value{
-A list with items "trend", "seasonal", and "yhat" containing 
+A list with items "trend", "seasonal", and "yhat" containing
  posterior predictive samples for that component.
 }
 \description{

R/man/sample_posterior_predictive.Rd

@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/prophet.R
+\name{sample_posterior_predictive}
+\alias{sample_posterior_predictive}
+\title{Prophet posterior predictive samples.}
+\usage{
+sample_posterior_predictive(m, df)
+}
+\arguments{
+\item{m}{Prophet object.}
+
+\item{df}{Prediction dataframe.}
+}
+\value{
+List with posterior predictive samples for each component.
+}
+\description{
+Prophet posterior predictive samples.
+}