Showing 13 of 30 files from the diff.
Newly tracked file
R/mc.R changed.
Newly tracked file
R/vfold.R changed.
Newly tracked file
R/lables.R changed.
Newly tracked file
R/apparent.R changed.
Newly tracked file
R/groups.R changed.
Newly tracked file
R/validation_split.R changed.
Newly tracked file
R/bootci.R changed.
Newly tracked file
R/initial_split.R changed.
Newly tracked file
R/rolling_origin.R changed.
Newly tracked file
R/complement.R changed.
Newly tracked file
R/loo.R changed.
Newly tracked file
R/slide.R changed.
Newly tracked file
R/nest.R changed.
Other files ignored by Codecov

@@ -66,7 +66,7 @@
Loading
66 66
              strata = strata,
67 67
              breaks = breaks)
68 68
69 -
  ## We remove the holdout indicies since it will save space and we can
69 +
  ## We remove the holdout indices since it will save space and we can
70 70
  ## derive them later when they are needed.
71 71
72 72
  split_objs$splits <- map(split_objs$splits, rm_out)

@@ -26,7 +26,7 @@
Loading
26 26
#' @export
27 27
#' @return A tibble with classes `vfold_cv`, `rset`, `tbl_df`, `tbl`, and
28 28
#'  `data.frame`. The results include a column for the data split objects and
29 -
#'  one or more identification variables. For a single repeats, there will be
29 +
#'  one or more identification variables. For a single repeat, there will be
30 30
#'  one column called `id` that has a character string with the fold identifier.
31 31
#'  For repeats, `id` is the repeat number and an additional column called `id2`
32 32
#'  that contains the fold information (within repeat).
@@ -85,7 +85,7 @@
Loading
85 85
    }
86 86
  }
87 87
88 -
  ## We remove the holdout indicies since it will save space and we can
88 +
  ## We remove the holdout indices since it will save space and we can
89 89
  ## derive them later when they are needed.
90 90
91 91
  split_objs$splits <- map(split_objs$splits, rm_out)

@@ -6,7 +6,7 @@
Loading
6 6
#'
7 7
#' @param object An `rset` object
8 8
#' @param make_factor A logical for whether the results should be
9 -
#'  character or a factor.
9 +
#'  a character or a factor.
10 10
#' @param ... Not currently used.
11 11
#' @return A single character or factor vector.
12 12
#' @export
@@ -64,9 +64,9 @@
Loading
64 64
## can have more than one element (in the case of nesting)
65 65
66 66
67 -
#' Short Decriptions of rsets
67 +
#' Short Descriptions of rsets
68 68
#'
69 -
#' Produce a chracter vector of describing the resampling method.
69 +
#' Produce a character vector describing the resampling method.
70 70
#'
71 71
#' @param x An `rset` object
72 72
#' @param ... Not currently used.
@@ -219,7 +219,7 @@
Loading
219 219
#'
220 220
#' For a data set, `add_resample_id()` will add at least one new column that
221 221
#'  identifies which resample that the data came from. In most cases, a single
222 -
#'  column is added but for some resampling methods two or more are added.
222 +
#'  column is added but for some resampling methods, two or more are added.
223 223
#' @param .data A data frame
224 224
#' @param split A single `rset` object.
225 225
#' @param dots A single logical: should the id columns be prefixed with a "."

@@ -1,7 +1,7 @@
Loading
1 1
#' Sampling for the Apparent Error Rate
2 2
#'
3 3
#' When building a model on a data set and re-predicting the same data, the
4 -
#'   performance estimate from those predictions is often call the
4 +
#'   performance estimate from those predictions is often called the
5 5
#'   "apparent" performance of the model. This estimate can be wildly
6 6
#'   optimistic. "Apparent sampling" here means that the analysis and
7 7
#'   assessment samples are the same. These resamples are sometimes used in

@@ -60,7 +60,7 @@
Loading
60 60
61 61
  split_objs <- group_vfold_splits(data = data, group = group, v = v)
62 62
63 -
  ## We remove the holdout indicies since it will save space and we can
63 +
  ## We remove the holdout indices since it will save space and we can
64 64
  ## derive them later when they are needed.
65 65
66 66
  split_objs$splits <- map(split_objs$splits, rm_out)

@@ -40,7 +40,7 @@
Loading
40 40
              strata = strata,
41 41
              breaks = breaks)
42 42
43 -
  ## We remove the holdout indicies since it will save space and we can
43 +
  ## We remove the holdout indices since it will save space and we can
44 44
  ## derive them later when they are needed.
45 45
46 46
  split_objs$splits <- map(split_objs$splits, rm_out)

@@ -174,7 +174,7 @@
Loading
174 174
#'  `TRUE` for the percentile method, the apparent data is never used in calculating
175 175
#'  the percentile confidence interval.
176 176
#' @param statistics An unquoted column name or `dplyr` selector that identifies
177 -
#'  a single column in the data set that contains the indiviual bootstrap
177 +
#'  a single column in the data set that contains the individual bootstrap
178 178
#'  estimates. This can be a list column of tidy tibbles (that contains columns
179 179
#'  `term` and `estimate`) or a simple numeric column. For t-intervals, a
180 180
#'  standard tidy column (usually called `std.err`) is required.

@@ -60,7 +60,7 @@
Loading
60 60
}
61 61
62 62
#' @rdname initial_split
63 -
#' @param lag A value to include an lag between the assessment
63 +
#' @param lag A value to include a lag between the assessment
64 64
#'  and analysis set. This is useful if lagged predictors will be used
65 65
#'  during training and testing.
66 66
#' @export

@@ -12,7 +12,7 @@
Loading
12 12
#' `skip` enables the function to not use every data point in the resamples.
13 13
#'  When `skip = 0`, the resampling data sets will increment by one position.
14 14
#'  Suppose that the rows of a data set are consecutive days. Using `skip = 6`
15 -
#'  will make the analysis data set operate on *weeks* instead of days. The
15 +
#'  will make the analysis data set to operate on *weeks* instead of days. The
16 16
#'  assessment set size is not affected by this option.
17 17
#' @seealso
18 18
#' [sliding_window()], [sliding_index()], and [sliding_period()] for additional
@@ -26,7 +26,7 @@
Loading
26 26
#' @param skip A integer indicating how many (if any) _additional_ resamples
27 27
#'  to skip to thin the total amount of data points in the analysis resample.
28 28
#' See the example below.
29 -
#' @param lag A value to include an lag between the assessment
29 +
#' @param lag A value to include a lag between the assessment
30 30
#'  and analysis set. This is useful if lagged predictors will be used
31 31
#'  during training and testing.
32 32
#' @export

@@ -2,7 +2,7 @@
Loading
2 2
#'
3 3
#' Given an `rsplit` object, `complement` will determine which
4 4
#'   of the data rows are contained in the assessment set. To save space,
5 -
#'   many of the `rset` objects will not contain indicies for the
5 +
#'   many of the `rset` objects will not contain indices for the
6 6
#'   assessment split.
7 7
#'
8 8
#' @param x An `rsplit` object
@@ -84,14 +84,14 @@
Loading
84 84
}
85 85
86 86
87 -
#' Add Assessment Indicies
87 +
#' Add Assessment Indices
88 88
#'
89 89
#' Many `rsplit` and `rset` objects do not contain indicators for
90 90
#'   the assessment samples. `populate()` can be used to fill the slot
91 91
#'   for the appropriate indices.
92 92
#' @param x A `rsplit` and `rset` object.
93 93
#' @param ... Not currently used
94 -
#' @return An object of the same kind with the integer indicies.
94 +
#' @return An object of the same kind with the integer indices.
95 95
#' @examples
96 96
#' set.seed(28432)
97 97
#' fold_rs <- vfold_cv(mtcars)

@@ -17,7 +17,7 @@
Loading
17 17
    list(splits = map(split_objs$splits, change_class),
18 18
         id = paste0("Resample", seq_along(split_objs$id)))
19 19
20 -
  ## We remove the holdout indicies since it will save space and we can
20 +
  ## We remove the holdout indices since it will save space and we can
21 21
  ## derive them later when they are needed.
22 22
23 23
  split_objs$splits <- map(split_objs$splits, rm_out)

@@ -117,7 +117,7 @@
Loading
117 117
#'   _in the time zone of the index_.
118 118
#'
119 119
#'   This is generally used to define the anchor time to count from,
120 -
#'   which is relevant when the every value is `> 1`.
120 +
#'   which is relevant when the `every` value is `> 1`.
121 121
#'
122 122
#' @seealso
123 123
#' [rolling_origin()]

@@ -14,7 +14,7 @@
Loading
14 14
#'   specified and, if it is given, will be ignored.
15 15
#' @param inside An expression for the type of resampling to be conducted
16 16
#'   within the initial procedure.
17 -
#' @return  An tibble with classe `nested_cv` and any other classes that
17 +
#' @return  An tibble with `nested_cv` class and any other classes that
18 18
#'   outer resampling process normally contains. The results include a
19 19
#'  column for the outer data split objects, one or more `id` columns,
20 20
#'  and a column of nested tibbles called `inner_resamples` with the
Files Coverage
R 81.90%
Project Totals (26 files) 81.90%
1
comment: false
2

3
coverage:
4
  status:
5
    project:
6
      default:
7
        target: auto
8
        threshold: 1%
9
        informational: true
10
    patch:
11
      default:
12
        target: auto
13
        threshold: 1%
14
        informational: true
Sunburst
The inner-most circle is the entire project, moving away from the center are folders then, finally, a single file. The size and color of each slice is representing the number of statements and the coverage, respectively.
Icicle
The top section represents the entire project. Proceeding with folders and finally individual files. The size and color of each slice is representing the number of statements and the coverage, respectively.
Grid
Each block represents a single file in the project. The size and color of each block is represented by the number of statements and the coverage, respectively.
Loading