Get WRTDS residuals for each quantile model. These are used to estimate goodness of fit of the model predictions.
wrtdsrsd(dat_in, ...)
# S3 method for tidal
wrtdsrsd(dat_in, trace = TRUE, ...)
# S3 method for tidalmean
wrtdsrsd(dat_in, trace = TRUE, ...)input tidal object which must already have fitted model data
arguments passed to or from other methods
logical indicating if progress is shown in the console
Columns are added to the data of the tidal object for residuals and non-conditional residuals. Both are required to assess the goodness of fit measure described for quantile regression in Koenker and Machado (1999).
A tidal object with columns added to the predonobs attribute for the residuals ('rsd') and non-conditional residuals ('rsdnl') of each quantile model or a tidalmean object with columns added to the predonobs attribute for the residuals ('rsd') and back-transformed residuals ('bt_rsd').
Koenker, R., Machado, J.A.F. 1999. Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association. 94(448):1296-1310.
## load a fitted model object
data(tidfit)
## run the function
res <- wrtdsrsd(tidfit)
head(res)
#> date res flo lim not_cens day_num month year
#> 1 1974-01-01 3.417727 0.4100012 0.8754687 TRUE 0.005479452 1 1974
#> 2 1974-02-01 3.860730 0.5086014 0.8754687 TRUE 0.090410959 2 1974
#> 3 1974-03-01 2.639057 0.3490211 0.8754687 TRUE 0.164383562 3 1974
#> 4 1974-04-01 2.484907 0.3509642 0.8754687 TRUE 0.249315068 4 1974
#> 5 1974-05-01 2.708050 0.3345128 0.8754687 TRUE 0.331506849 5 1974
#> 6 1974-06-01 2.740840 0.3009137 0.8754687 TRUE 0.416438356 6 1974
#> dec_time fit0.1 fit0.5 fit0.9 norm0.1 norm0.5 norm0.9
#> 1 1974.005 2.610429 3.267295 3.685544 2.570321 3.083240 3.319924
#> 2 1974.090 2.541529 3.157350 3.831174 2.555630 3.008398 3.435083
#> 3 1974.164 2.545622 2.838006 3.280516 2.598238 2.863887 3.368876
#> 4 1974.249 2.511704 2.649719 3.233701 2.581767 2.717951 3.281687
#> 5 1974.332 2.576503 2.709492 3.360528 2.593957 2.734914 3.346043
#> 6 1974.416 2.747688 2.811803 3.463858 2.745408 2.825647 3.474432