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26 | 26 | #' column, `..y`.
|
27 | 27 | #' }
|
28 | 28 | #'
|
29 |
| -#' For example, if you use the model formula `mpg ~ .` with the `mtcars` |
30 |
| -#' data, the values would be |
| 29 | +#' For example, if you use the model formula `circumference ~ .` with the |
| 30 | +#' built-in `Orange` data, the values would be |
31 | 31 | #' \preformatted{
|
32 |
| -#' .preds() = 10 (the 10 columns in `mtcars`) |
33 |
| -#' .cols() = 10 (10 numeric columns + 0 from dummy variables) |
34 |
| -#' .obs() = 32 |
| 32 | +#' .preds() = 2 (the 2 remaining columns in `Orange`) |
| 33 | +#' .cols() = 5 (1 numeric column + 4 from Tree dummy variables) |
| 34 | +#' .obs() = 35 |
35 | 35 | #' .lvls() = NA (no factor outcome)
|
36 |
| -#' .facts() = 0 (no factor outcome) |
37 |
| -#' .y() = <vector> (mpg as a vector) |
38 |
| -#' .x() = <data.frame> (The other 10 columns as a data frame) |
| 36 | +#' .facts() = 1 (the Tree predictor) |
| 37 | +#' .y() = <vector> (circumference as a vector) |
| 38 | +#' .x() = <data.frame> (The other 2 columns as a data frame) |
39 | 39 | #' .dat() = <data.frame> (The full data set)
|
40 | 40 | #' }
|
41 | 41 | #'
|
42 |
| -#' If the formula `as.character(cyl) ~ .` where used: |
| 42 | +#' If the formula `Tree ~ .` were used: |
43 | 43 | #' \preformatted{
|
44 |
| -#' .preds() = 10 (the 10 numeric columns in `mtcars`) |
45 |
| -#' .cols() = 10 (same) |
46 |
| -#' .obs() = 32 |
47 |
| -#' .lvls() = c("4" = 11, "6" = 7, "8" = 14) |
| 44 | +#' .preds() = 2 (the 2 numeric columns in `Orange`) |
| 45 | +#' .cols() = 2 (same) |
| 46 | +#' .obs() = 35 |
| 47 | +#' .lvls() = c("1" = 7, "2" = 7, "3" = 7, "4" = 7, "5" = 7) |
48 | 48 | #' .facts() = 0
|
49 |
| -#' .y() = <vector> (as.character(cyl) as a vector) |
50 |
| -#' .x() = <data.frame> (The other 10 columns as a data frame) |
| 49 | +#' .y() = <vector> (Tree as a vector) |
| 50 | +#' .x() = <data.frame> (The other 2 columns as a data frame) |
51 | 51 | #' .dat() = <data.frame> (The full data set)
|
52 | 52 | #' }
|
53 | 53 | #'
|
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