ICBioMark (version 0.1.4)

pred_intervals: Produce Error Bounds for Predictions

Description

A function to produce a confidence region for a linear predictor. In upcoming versions will (hopefully) be greatly simplified.

Usage

pred_intervals(
  predictions,
  pred_model,
  gen_model,
  training_matrix,
  gene_lengths,
  biomarker_values,
  alpha = 0.1,
  range_factor = 1.1,
  s = NULL,
  max_panel_length = NULL,
  biomarker = "TMB",
  marker_mut_types = c("NS", "I"),
  model = "Refitted T"
)

Arguments

predictions

(list) A predictions object, as produced by get_predictions().

pred_model

(list) A predictive model, as produced by pred_first_fit(), pred_refit_panel() or pred_refit_range().

gen_model

(list) A generative model, as produce by fit_gen_model

training_matrix

(sparse matrix) A training matrix, as produced by get_tables()$matrix or get_table_from_maf()$matrix.

gene_lengths

(data frame) A data frame with columns 'Hugo_Symbol' and 'max_cds'. See example_maf_data$gene_lengths, or ensembl_gene_lengths for examples.

biomarker_values

(data frame) A data frame containing the true values of the biomarker in question.

alpha

(numeric) Confidence level for error bounds.

range_factor

(numeric) Value specifying how far beyond the range of max(biomarker) to plot confidence region.

s

(numeric) If input predictions are for a range of panels, s chooses which panel (column in a pred_fit object) to produce predictions for.

max_panel_length

(numeric) Select panel by maximum length.

biomarker

(character) Which biomarker is being predicted.

marker_mut_types

(character) If biomarker is not one of "TMB" or "TIB", then this is required to specify which mutation type groups constitute the biomarker.

model

(character) The model (must be based on a linear estimator) for which prediction intervals are being generated.

Value

A list with two entries:

• prediction_intervals:

• confidence_region:

Examples

# NOT RUN {
example_intervals <- pred_intervals(predictions = get_predictions(example_refit_range,
               new_data = example_tables$val),
               pred_model = example_refit_range, biomarker_values = example_tmb_tables$val,
               gen_model = example_gen_model, training_matrix = example_tables$train$matrix,
               max_panel_length = 15000, gene_lengths = example_maf_data$gene_lengths)

example_confidence_plot <- ggplot2::ggplot() +
  ggplot2::geom_point(data = example_intervals$prediction_intervals,
             ggplot2::aes(x = true_value, y = estimated_value)) +
        ggplot2::geom_ribbon(data = example_intervals$confidence_region,
          ggplot2::aes(x = x, ymin = y_lower, ymax = y_upper),
                    fill = "red", alpha = 0.2) +
        ggplot2::geom_line(data = example_intervals$confidence_region,
          ggplot2::aes(x = x, y = y), linetype = 2) +
        ggplot2::scale_x_log10() + ggplot2::scale_y_log10()

plot(example_confidence_plot)
# }