我想使用该tsibble包拟合线性回归模型，并且我有一堆要包含在分析中的虚拟变量。示例数据集如下：
library(tsibble)
library(dplyr)
library(fable)
ex = structure(list(id = c("KEY1", "KEY1", "KEY1", "KEY1", "KEY1",
"KEY1", "KEY1", "KEY1", "KEY1", "KEY1", "KEY1", "KEY1", "KEY1",
"KEY1", "KEY1"), sales = c(0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), date = structure(c(15003, 15004, 15005, 15006, 15007,
15008, 15009, 15010, 15011, 15012, 15013, 15014, 15015, 15016,
15017), class = "Date"), wday = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L), dummy_1 = c(0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), dummy_2 = c(0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0), dummy_3 = c(0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -15L), key = structure(list(
id = "KEY1", .rows = list(1:15)), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), index = structure("date", ordered = TRUE), index2 = "date", interval = structure(list(
year = 0, quarter = 0, month = 0, week = 0, day = 1, hour = 0,
minute = 0, second = 0, millisecond = 0, microsecond = 0,
nanosecond = 0, unit = 0), class = "interval"), class = c("tbl_ts",
"tbl_df", "tbl", "data.frame"))
> ex
# A tsibble: 15 x 7 [1D]
# Key: id [1]
id sales date wday dummy_1 dummy_2 dummy_3
<chr> <dbl> <date> <int> <dbl> <dbl> <dbl>
1 KEY1 0 2011-01-29 1 0 0 0
2 KEY1 5 2011-01-30 2 0 0 0
3 KEY1 0 2011-01-31 3 0 0 1
4 KEY1 0 2011-02-01 4 1 0 0
5 KEY1 0 2011-02-02 5 0 0 0
6 KEY1 0 2011-02-03 6 0 0 0
7 KEY1 0 2011-02-04 7 0 1 0
8 KEY1 0 2011-02-05 1 0 0 0
9 KEY1 0 2011-02-06 2 0 0 0
10 KEY1 0 2011-02-07 3 0 0 0
11 KEY1 0 2011-02-08 4 0 0 0
12 KEY1 0 2011-02-09 5 0 0 0
13 KEY1 0 2011-02-10 6 0 0 0
14 KEY1 0 2011-02-11 7 0 0 0
15 KEY1 0 2011-02-12 1 0 0 0
它们太多了，无法手动指定，所以我希望能有更快的速度。通常我会通过.以下方式使用公式中的符号：
fit = ex %>%
model(TSLM(sales ~ trend() + season() + .))
但这不起作用：
Warning message:
1 error encountered for TSLM(sales ~ trend() + season() + .)
[1] '.' in formula and no 'data' argument
是否有tsibble解决此问题的系统方法，还是我必须使用数据集的名称动态创建公式？