Tidy into a joint model

Tidy or augment and then join objects of class radf_obj and radf_cv. The object of reference is the radf_cv. For example, if panel critical values are provided the function will return the panel test statistic.

Usage

# S3 method for class 'radf_obj'
tidy_join(x, y = NULL, ...)

# S3 method for class 'radf_obj'
augment_join(x, y = NULL, trunc = TRUE, ...)

Arguments

x

An object of class radf_obj.

y

An object of class radf_cv. The output will depend on the type of critical value.

...

Further arguments passed to methods. Not used.

trunc

Whether to remove the period of the minimum window from the plot (default = TRUE).

Details

tidy_join also calls augment_join when cv is of class sb_cv.

Examples

# \donttest{
rsim_data <- radf(sim_data, minw = 20)
cv <- radf_wb_cv(sim_data, minw = 20)

# One row per series/statistic, statistic and critical value side by side
tidy_join(rsim_data, cv)
#> # A tibble: 45 × 5
#>    id    stat  tstat sig      crit
#>    <fct> <fct> <dbl> <fct>   <dbl>
#>  1 psy1  adf   -2.46 90    -0.635 
#>  2 psy1  adf   -2.46 95    -0.462 
#>  3 psy1  adf   -2.46 99     0.0862
#>  4 psy1  sadf   1.95 90     1.49  
#>  5 psy1  sadf   1.95 95     1.97  
#>  6 psy1  sadf   1.95 99     3.05  
#>  7 psy1  gsadf  5.19 90     2.77  
#>  8 psy1  gsadf  5.19 95     3.39  
#>  9 psy1  gsadf  5.19 99     5.16  
#> 10 psy2  adf   -2.86 90    -0.598 
#> # ℹ 35 more rows

# summary() and diagnostics() are themselves built on top of tidy_join()
summary(rsim_data, cv = cv)
#> 
#> ── Summary (minw = 20, lag = 0) ──────────────── Wild Bootstrap (nboot = 500) ──
#> 
#> psy1 :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -2.46 -0.635 -0.462 0.0862
#> 2 sadf   1.95  1.49   1.97  3.05  
#> 3 gsadf  5.19  2.77   3.39  5.16  
#> 
#> psy2 :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -2.86 -0.598 -0.498 -0.157
#> 2 sadf   7.88  3.06   3.92   5.33 
#> 3 gsadf  7.88  4.04   4.86   6.29 
#> 
#> evans :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -5.83 -0.496 -0.323 -0.122
#> 2 sadf  -2.73  4.75   6.61  12.7  
#> 3 gsadf  5.47  7.77   9.63  14.8  
#> 
#> div :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`  `99`
#>   <fct> <dbl>  <dbl>  <dbl> <dbl>
#> 1 adf   -1.95 -0.359 0.0473 0.409
#> 2 sadf   1.11  0.926 1.30   1.91 
#> 3 gsadf  1.11  1.76  2.11   2.50 
#> 
#> blan :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -5.15 -0.332 -0.102  0.315
#> 2 sadf   3.93  3.45   4.74   7.70 
#> 3 gsadf 11.0   6.41   7.53  11.4  
#> 
# }

# \donttest{
rsim_data <- radf(sim_data, minw = 20)
cv <- radf_wb_cv(sim_data, minw = 20)

# Full statistic-path/critical-value-path join -- the table autoplot() is built on
aj <- augment_join(rsim_data, cv)
aj
#> # A tibble: 2,400 × 8
#>      key index id     data stat   tstat sig      crit
#>    <int> <dbl> <fct> <dbl> <fct>  <dbl> <fct>   <dbl>
#>  1    21    21 psy1  110.  badf  -2.36  90    -0.198 
#>  2    22    22 psy1   98.1 badf  -2.49  90    -0.129 
#>  3    23    23 psy1   91.0 badf  -2.26  90    -0.126 
#>  4    24    24 psy1   80.4 badf  -1.63  90    -0.132 
#>  5    25    25 psy1   69.2 badf  -0.815 90    -0.0975
#>  6    26    26 psy1   72.3 badf  -0.960 90    -0.164 
#>  7    27    27 psy1   67.7 badf  -0.693 90    -0.340 
#>  8    28    28 psy1   69.1 badf  -0.771 90    -0.388 
#>  9    29    29 psy1   65.4 badf  -0.609 90    -0.364 
#> 10    30    30 psy1   72.4 badf  -0.939 90    -0.403 
#> # ℹ 2,390 more rows

# Reproduce (a simplified version of) autoplot()'s own bsadf-vs-crit line plot
library(ggplot2)
aj %>%
  dplyr::filter(sig == 95, stat == "bsadf") %>%
  tidyr::pivot_longer(c(tstat, crit), names_to = "series") %>%
  ggplot(aes(index, value, col = series)) +
  geom_line() +
  facet_wrap(~id, scales = "free")

# }