Add the estimated fitted values back to the original
add_to_original_data.Rd
Add the estimated fitted values back to the original
Usage
add_to_original_data(
clean_data,
isat_object,
dep_var_basename = "imports_of_goods_and_services",
ardl_or_ecm = "ardl"
)
Arguments
- clean_data
An input data.frame or tibble. Must be the output of clean_data() to fit all requirements.
- isat_object
An object of class 'isat'. Most likely should be the 'best_model' element that is returned by the 'estimate_module()' function.
- dep_var_basename
A character string of the name of the dependent variable as contained in clean_data() in a level form (i.e. no ln or D in front of the name).
- ardl_or_ecm
Either 'ardl' or 'ecm' to determine whether to estimate the model as an Autoregressive Distributed Lag Function (ardl) or as an Equilibrium Correction Model (ecm).
Examples
sample_data <- dplyr::tibble(
time = rep(seq.Date(
from = as.Date("2000-01-01"),
to = as.Date("2000-12-31"), by = 1
), each = 2),
na_item = rep(c("yvar", "xvar"), 366), values = rnorm(366 * 2, mean = 100)
)
sample_data_clean <- osem:::clean_data(sample_data, max.ar = 4, max.dl = 4)
estimation <- osem:::estimate_module(sample_data_clean, "yvar", "xvar")
#> No Outliers or Step-Shifts detected in the marginal equations to test for Super Exogeneity in ln.yvar.
#> Hence not possible to run the test.
osem:::add_to_original_data(
sample_data_clean, estimation$best_model,
dep_var_basename = "yvar")
#> # A tibble: 366 × 48
#> index time trend yvar xvar ln.yvar ln.xvar D.yvar D.xvar D.ln.yvar
#> <int> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2000-01-01 1 98.6 100. 4.59 4.61 NA NA NA
#> 2 2 2000-01-02 2 97.6 100. 4.58 4.61 -1.04 -0.261 -0.0106
#> 3 3 2000-01-03 3 101. 101. 4.61 4.62 3.06 1.15 0.0309
#> 4 4 2000-01-04 4 98.2 99.8 4.59 4.60 -2.44 -1.40 -0.0246
#> 5 5 2000-01-05 5 99.8 99.7 4.60 4.60 1.58 -0.0354 0.0159
#> 6 6 2000-01-06 6 99.4 101. 4.60 4.61 -0.309 0.912 -0.00311
#> 7 7 2000-01-07 7 102. 98.4 4.63 4.59 2.62 -2.26 0.0260
#> 8 8 2000-01-08 8 101. 98.1 4.61 4.59 -1.55 -0.232 -0.0153
#> 9 9 2000-01-09 9 99.5 99.9 4.60 4.60 -1.03 1.81 -0.0103
#> 10 10 2000-01-10 10 101. 99.1 4.61 4.60 1.07 -0.861 0.0106
#> # ℹ 356 more rows
#> # ℹ 38 more variables: D.ln.xvar <dbl>, L1.yvar <dbl>, L1.xvar <dbl>,
#> # L1.ln.yvar <dbl>, L1.ln.xvar <dbl>, L1.D.yvar <dbl>, L1.D.xvar <dbl>,
#> # L1.D.ln.yvar <dbl>, L1.D.ln.xvar <dbl>, L2.yvar <dbl>, L2.xvar <dbl>,
#> # L2.ln.yvar <dbl>, L2.ln.xvar <dbl>, L2.D.yvar <dbl>, L2.D.xvar <dbl>,
#> # L2.D.ln.yvar <dbl>, L2.D.ln.xvar <dbl>, L3.yvar <dbl>, L3.xvar <dbl>,
#> # L3.ln.yvar <dbl>, L3.ln.xvar <dbl>, L3.D.yvar <dbl>, L3.D.xvar <dbl>, …