Evaluate the performance of different regression models
evalreg( dataset, pred, rvar, train = "All", data_filter = "", envir = parent.frame() )
| dataset | Dataset |
|---|---|
| pred | Predictions or predictors |
| rvar | Response variable |
| train | Use data from training ("Training"), test ("Test"), both ("Both"), or all data ("All") to evaluate model evalreg |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "training == 1") |
| envir | Environment to extract data from |
A list of results
Evaluate different regression models based on predictions. See https://radiant-rstats.github.io/docs/model/evalreg.html for an example in Radiant
summary.evalreg to summarize results
plot.evalreg to plot results
data.frame(price = diamonds$price, pred1 = rnorm(3000), pred2 = diamonds$price) %>% evalreg(pred = c("pred1", "pred2"), "price") %>% str()#> List of 10 #> $ rv : int [1:3000] 580 650 630 706 1080 3082 3328 4229 1895 3546 ... #> $ dat :'data.frame': 2 obs. of 6 variables: #> ..$ Type : chr [1:2] "All" "All" #> ..$ Predictor: chr [1:2] "pred1" "pred2" #> ..$ n : int [1:2] 3000 3000 #> ..$ Rsq : num [1:2] 0.00000153 1 #> ..$ RMSE : num [1:2] 5560 0 #> ..$ MAE : num [1:2] 3907 0 #> $ vars : chr [1:3] "pred1" "pred2" "price" #> $ df_name : chr "." #> $ dataset :'data.frame': 3000 obs. of 3 variables: #> ..$ price: int [1:3000] 580 650 630 706 1080 3082 3328 4229 1895 3546 ... #> ..$ pred1: num [1:3000] 0.16 1.014 0.897 1.867 1.65 ... #> ..$ pred2: int [1:3000] 580 650 630 706 1080 3082 3328 4229 1895 3546 ... #> $ pred : chr [1:2] "pred1" "pred2" #> $ rvar : chr "price" #> $ train : chr "All" #> $ data_filter: chr "" #> $ envir :<environment: 0x11b2fc38> #> - attr(*, "class")= chr [1:2] "evalreg" "list"