R（寓言包）中的预测：寓言中的准确度函数找不到 y 变量

Question

我正在尝试从 fable 包中精确功能。它有时会出现这样的意外错误

"Error: Could not find response variable(s) in the fable: Trips"

（这是来自https://otexts.com/fpp3/toolbox-exercises.html 的示例 12）

有人遇到过这个问题吗？ 这是我正在使用的代码：

# reprex is one of the (many) packages installed when you install tidyverse
#install.packages("tidyverse")
#install.packages("shiny")
#install.packages("htmltools")
#library(shiny)
#library(miniUI)

# install reprex by itself
library(reprex)
library(fpp3)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── fpp3 0.3 ──
#> ✓ tibble      3.0.1     ✓ tsibble     0.9.0
#> ✓ dplyr       1.0.0     ✓ tsibbledata 0.2.0
#> ✓ tidyr       1.1.0     ✓ feasts      0.1.3
#> ✓ lubridate   1.7.9     ✓ fable       0.2.0
#> ✓ ggplot2     3.3.1
#> ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── fpp3_conflicts ──
#> x lubridate::date()   masks base::date()
#> x dplyr::filter()     masks stats::filter()
#> x tsibble::interval() masks lubridate::interval()
#> x dplyr::lag()        masks stats::lag()
gc_tourism <- tourism %>% filter(Region=='Gold Coast') %>%
  group_by(Purpose) %>%
  summarise(Trips=sum(Trips))
gc_train_1 <- gc_tourism %>% 
  group_by(Purpose) %>%
  slice(1:(n()-4))
fit1 <- gc_train_1 %>%
  model(SNAIVE=SNAIVE(Trips))
gc_fc_1 <- fit1 %>% forecast(h=4)
gc_fc_1 %>% accuracy(gc_tourism)
#> Error: Could not find response variable(s) in the fable: Trips

^{由reprex package (v0.3.0) 于 2020 年 6 月 28 日创建}

Answer 1

在将 tsibble 和 fable 软件包升级到最新版本（分别为 0.9.1 和 0.2.1）后，我能够解决此问题。

rm(list=ls())
library(fpp3)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── fpp3 0.3 ──
#> ✓ tibble      3.0.1     ✓ tsibble     0.9.1
#> ✓ dplyr       1.0.0     ✓ tsibbledata 0.2.0
#> ✓ tidyr       1.1.0     ✓ feasts      0.1.4
#> ✓ lubridate   1.7.9     ✓ fable       0.2.1
#> ✓ ggplot2     3.3.2
#> ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── fpp3_conflicts ──
#> x lubridate::date()   masks base::date()
#> x dplyr::filter()     masks stats::filter()
#> x tsibble::interval() masks lubridate::interval()
#> x dplyr::lag()        masks stats::lag()
library(reprex)

gc_tourism <- tourism %>% dplyr::filter(Region=='Gold Coast') %>%
  dplyr::group_by(Purpose) %>%
  dplyr::summarise(Trips=sum(Trips))
gc_train_1 <- gc_tourism %>% 
  dplyr::group_by(Purpose) %>%
  dplyr::slice(1:(n()-4))
fit1 <- gc_train_1 %>%
  model(SNAIVE=SNAIVE(Trips))
gc_fc_1 <- fit1 %>% forecast(h=4)
gc_fc_1 %>% accuracy(gc_tourism)
#> # A tibble: 4 x 10
#>   .model Purpose  .type    ME  RMSE   MAE   MPE  MAPE  MASE    ACF1
#>   <chr>  <chr>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>
#> 1 SNAIVE Business Test  17.4  41.3  38.6   9.63  35.4 1.81  -0.276 
#> 2 SNAIVE Holiday  Test  38.9  73.9  70.7   6.42  13.0 1.42  -0.213 
#> 3 SNAIVE Other    Test  -1.50  6.27  5.61 -9.01  17.3 0.493 -0.0655
#> 4 SNAIVE Visiting Test  20.4  64.2  59.0   5.41  17.8 1.39  -0.574


gc_tourism <- tourism %>% dplyr::filter(Region=='Gold Coast') %>%
  group_by(Purpose) %>%
  summarise(Trips=sum(Trips))

gc_train_1 <- gc_tourism %>% 
  group_by(Purpose) %>%
  slice(1:(n()-4))
fit1 <- gc_train_1 %>%
  model(SNAIVE=SNAIVE(Trips))
gc_fc_1 <- fit1 %>% forecast(h=4)
gc_fc_1 %>% accuracy(gc_tourism)
#> # A tibble: 4 x 10
#>   .model Purpose  .type    ME  RMSE   MAE   MPE  MAPE  MASE    ACF1
#>   <chr>  <chr>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>
#> 1 SNAIVE Business Test  17.4  41.3  38.6   9.63  35.4 1.81  -0.276 
#> 2 SNAIVE Holiday  Test  38.9  73.9  70.7   6.42  13.0 1.42  -0.213 
#> 3 SNAIVE Other    Test  -1.50  6.27  5.61 -9.01  17.3 0.493 -0.0655
#> 4 SNAIVE Visiting Test  20.4  64.2  59.0   5.41  17.8 1.39  -0.574

^{由reprex package (v0.3.0) 于 2020-07-01 创建}

Answer 2

当您致电library(fpp3) 时，您会收到一条警告（冲突），说明与dpylr::filter 存在冲突。这意味着您必须使用dyplr::filter 调用filter，如此reprex 所示。

library(fpp3)
#> ── Attaching packages ─────────────── fpp3 0.3 ──
#> ✓ tibble      3.0.1     ✓ tsibble     0.9.1
#> ✓ dplyr       1.0.0     ✓ tsibbledata 0.2.0
#> ✓ tidyr       1.1.0     ✓ feasts      0.1.4
#> ✓ lubridate   1.7.4     ✓ fable       0.2.1
#> ✓ ggplot2     3.3.2
#> ── Conflicts ────────────────── fpp3_conflicts ──
#> x lubridate::date()       masks base::date()
#> x dplyr::filter()         masks stats::filter()
#> x tsibble::interval()     masks lubridate::interval()
#> x dplyr::lag()            masks stats::lag()
#> x tsibble::new_interval() masks lubridate::new_interval()

gc_tourism <- tourism %>% dplyr::filter(Region=='Gold Coast') %>%
  group_by(Purpose) %>%
  summarise(Trips=sum(Trips))

gc_train_1 <- gc_tourism %>% 
  group_by(Purpose) %>%
  slice(1:(n()-4))
fit1 <- gc_train_1 %>%
  model(SNAIVE=SNAIVE(Trips))
gc_fc_1 <- fit1 %>% forecast(h=4)
gc_fc_1 %>% accuracy(gc_tourism)
#> # A tibble: 4 x 10
#>   .model Purpose  .type    ME  RMSE   MAE   MPE  MAPE  MASE    ACF1
#>   <chr>  <chr>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>
#> 1 SNAIVE Business Test  17.4  41.3  38.6   9.63  35.4 1.81  -0.276 
#> 2 SNAIVE Holiday  Test  38.9  73.9  70.7   6.42  13.0 1.42  -0.213 
#> 3 SNAIVE Other    Test  -1.50  6.27  5.61 -9.01  17.3 0.493 -0.0655
#> 4 SNAIVE Visiting Test  20.4  64.2  59.0   5.41  17.8 1.39  -0.574