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@@ -66,7 +66,7 @@
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  res <- do.call(rbind, res)
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  res <- tapply(res$.estimate, res$.metric, mean)
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  data.frame(metric = row.names(res), score = res)
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  tibble::tibble(metric = row.names(res), score = res)
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}
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@@ -75,7 +75,7 @@
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#' @aliases evaluate_aic
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evaluate_aic <- function(model, data) {
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  full_model_fit <- model$fit(data)
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  data.frame(
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  tibble::tibble(
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    metric = "aic",
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    score = stats::AIC(full_model_fit$fitted_model)
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  )
@@ -91,14 +91,27 @@
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    function(model) safely(method)(model, data, ...)
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  )
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  out <- base_transpose(out)
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  out <- tibble::tibble(
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    model_name = names(models),
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    model = models,
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    data = list(data),
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    result = out[[1]],
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    warning = out[[2]],
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    error = out[[3]]
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  )
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  nms <- names(models)
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  if (is.null(nms)) {
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    out <- tibble::tibble(
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      model = models,
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      data = list(data),
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      result = out[[1]],
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      warning = out[[2]],
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      error = out[[3]]
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    )  
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  } else {
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    out <- tibble::tibble(
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      model_name = nms,
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      model = models,
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      data = list(data),
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      result = out[[1]],
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      warning = out[[2]],
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      error = out[[3]]
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    )
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  }
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  tidyr::unnest(out, "result", keep_empty = TRUE)
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}
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Project Totals (2 files) 79.17%

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