| 66 | 66 | ||
| 67 | 67 | res <- do.call(rbind, res) |
|
| 68 | 68 | res <- tapply(res$.estimate, res$.metric, mean) |
|
| 69 | - | data.frame(metric = row.names(res), score = res) |
|
| 69 | + | tibble::tibble(metric = row.names(res), score = res) |
|
| 70 | 70 | } |
|
| 71 | 71 | ||
| 72 | 72 |
| 75 | 75 | #' @aliases evaluate_aic |
|
| 76 | 76 | evaluate_aic <- function(model, data) { |
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| 77 | 77 | full_model_fit <- model$fit(data) |
|
| 78 | - | data.frame( |
|
| 78 | + | tibble::tibble( |
|
| 79 | 79 | metric = "aic", |
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| 80 | 80 | score = stats::AIC(full_model_fit$fitted_model) |
|
| 81 | 81 | ) |
| 91 | 91 | function(model) safely(method)(model, data, ...) |
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| 92 | 92 | ) |
|
| 93 | 93 | out <- base_transpose(out) |
|
| 94 | - | out <- tibble::tibble( |
|
| 95 | - | model_name = names(models), |
|
| 96 | - | model = models, |
|
| 97 | - | data = list(data), |
|
| 98 | - | result = out[[1]], |
|
| 99 | - | warning = out[[2]], |
|
| 100 | - | error = out[[3]] |
|
| 101 | - | ) |
|
| 94 | + | ||
| 95 | + | nms <- names(models) |
|
| 96 | + | if (is.null(nms)) { |
|
| 97 | + | out <- tibble::tibble( |
|
| 98 | + | model = models, |
|
| 99 | + | data = list(data), |
|
| 100 | + | result = out[[1]], |
|
| 101 | + | warning = out[[2]], |
|
| 102 | + | error = out[[3]] |
|
| 103 | + | ) |
|
| 104 | + | } else { |
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| 105 | + | out <- tibble::tibble( |
|
| 106 | + | model_name = nms, |
|
| 107 | + | model = models, |
|
| 108 | + | data = list(data), |
|
| 109 | + | result = out[[1]], |
|
| 110 | + | warning = out[[2]], |
|
| 111 | + | error = out[[3]] |
|
| 112 | + | ) |
|
| 113 | + | } |
|
| 114 | + | ||
| 102 | 115 | ||
| 103 | 116 | tidyr::unnest(out, "result", keep_empty = TRUE) |
|
| 104 | 117 | } |
| Files | Coverage |
|---|---|
| R | 79.17% |
| Project Totals (2 files) | 79.17% |
tjtnew
