Assess a trained elastic net model

This function assesses a trained `tof_model`'s performance on new data by computing model type-specific performance measurements. If new data isn't provided, performance metrics for the training data will be provided.

Usage

tof_assess_model(tof_model, new_data)

Arguments

tof_model: A `tof_model` trained using tof_train_model
new_data: A tibble of new observations that should be used to evaluate the `tof_model`'s performance. If new_data isn't provided, model evaluation will will be performed using the training data used to fit the model. Alternatively, the string "tuning" can be provided to access the model's performance metrics during the (resampled) model tuning process.

Value

A list of performance metrics whose components depend on the model type:

"model_metrics": A tibble with two columns ("metric" and "value") containing standard performance metrics for each model type. For linear models, the "mse" (the mean squared error of the predictions) and "mae" (the mean absolute error of the predictions). For two-class models, "roc_auc" (the area under the Receiver-Operating Curve for the classification), "misclassification error" (the proportion of misclassified observations), "binomial_deviance" (see deviance.glmnet), "mse" (the mean squared error of the logit function), and "mae" (the mean absolute error of the logit function). For multiclass models, "roc_auc" (the area under the Receiver-Operating Curve for the classification using the Hand-Till generalization of the ROC AUC for multiclass models in roc_auc), "misclassification error" (the proportion of misclassified observations), "multinomial_deviance" (see deviance.glmnet), and "mse" and "mae" as above. For survival models, "concordance_index" (Harrel's C index; see deviance.glmnet) and "partial_likelihood_deviance" (see deviance.glmnet).
"roc_curve": Reported only for "two-class" and "multiclass" models. For both, a tibble is provided reporting the true-positive rate (tpr) and false-positive rate (fpr) at each threshold for classification for use in plotting a receiver-operating curve. For "multiclass" models, the ".level" column allows for separating the values in roc_curve such that one ROC can be plotted for each class.
"confusion_matrix": Reported only for "two-class" and "multiclass" models. For both, a tibble is provided reporting the "confusion matrix" of the classification in long-format.
"survival_curves": Reported only for "survival" models. A tibble indicating each patient's probability of survival (1 - probability(event)) at each timepoint in the dataset and whether each sample was placed in the "high" or "low" risk group according to its predicted relative risk (and the tof_model's optimal relative_risk cutoff in the training dataset).

Examples

feature_tibble <-
    dplyr::tibble(
        sample = as.character(1:100),
        cd45 = runif(n = 100),
        pstat5 = runif(n = 100),
        cd34 = runif(n = 100),
        outcome = (3 * cd45) + (4 * pstat5) + rnorm(100)
    )

new_tibble <-
    dplyr::tibble(
        sample = as.character(1:20),
        cd45 = runif(n = 20),
        pstat5 = runif(n = 20),
        cd34 = runif(n = 20),
        outcome = (3 * cd45) + (4 * pstat5) + rnorm(20)
    )

split_data <- tof_split_data(feature_tibble, split_method = "simple")

# train a regression model
regression_model <-
    tof_train_model(
        split_data = split_data,
        predictor_cols = c(cd45, pstat5, cd34),
        response_col = outcome,
        model_type = "linear"
    )

# assess the model on new data
tof_assess_model(tof_model = regression_model, new_data = new_tibble)
#> $model_metrics
#> # A tibble: 2 × 2
#>   metric value
#>   <chr>  <dbl>
#> 1 mse    0.795
#> 2 mae    0.788
#>

Usage

Arguments

Value

See also

Examples