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@@ -1592,7 +1592,8 @@ seasonality_plot_df <- function(m, ds) {
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#' @keywords internal
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#' @keywords internal
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plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
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plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
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# Compute weekly seasonality for a Sun-Sat sequence of dates.
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# Compute weekly seasonality for a Sun-Sat sequence of dates.
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- days <- seq(set_date('2017-01-01'), by='d', length.out=7) + weekly_start
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+ days <- seq(set_date('2017-01-01'), by='d', length.out=7) + as.difftime(
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+ weekly_start, units = "days")
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df.w <- seasonality_plot_df(m, days)
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df.w <- seasonality_plot_df(m, days)
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seas <- predict_seasonal_components(m, df.w)
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seas <- predict_seasonal_components(m, df.w)
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seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))
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seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))
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@@ -1625,7 +1626,8 @@ plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
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#' @keywords internal
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#' @keywords internal
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plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
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plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
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# Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
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# Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
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- days <- seq(set_date('2017-01-01'), by='d', length.out=365) + yearly_start
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+ days <- seq(set_date('2017-01-01'), by='d', length.out=365) + as.difftime(
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+ yearly_start, units = "days")
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df.y <- seasonality_plot_df(m, days)
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df.y <- seasonality_plot_df(m, days)
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seas <- predict_seasonal_components(m, df.y)
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seas <- predict_seasonal_components(m, df.y)
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seas$ds <- df.y$ds
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seas$ds <- df.y$ds
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