inst/doc/ROCnGO.R

In ROCnGO: Fast Analysis of ROC Curves

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup, warning=FALSE, message=FALSE--------------------------------------
library(ROCnGO)
library(dplyr)
library(forcats)

## -----------------------------------------------------------------------------
# Filter cases of versicolor species
iris_subset <- as_tibble(iris) %>% filter(Species != "versicolor")
iris_subset

## ----warning=FALSE------------------------------------------------------------
# Calculate ROC points for Sepal.Lenght
points <- roc_points(
  data = iris_subset,
  predictor = Sepal.Length,
  response = Species
)
points

# Plot points
plot(points$fpr, points$tpr)

## ----warning=FALSE------------------------------------------------------------
# Check response levels
levels(iris_subset$Species)

# Set virginica as first value in levels
iris_subset$Species <- fct_relevel(iris_subset$Species, "virginica")
levels(iris_subset$Species)

# Plot ROC curve
points <- roc_points(
  data = iris_subset,
  predictor = Sepal.Length,
  response = Species
)
plot(points$fpr, points$tpr)

## ----warning=FALSE------------------------------------------------------------
# Calc partial ROC points
p_points <- calc_partial_roc_points(
  data = iris_subset,
  predictor = Sepal.Length,
  response = Species,
  lower_threshold = 0.9,
  upper_threshold = 1,
  ratio = "tpr"
)
p_points

# Plot partial ROC curve
plot(p_points$fpr, p_points$tpr)

## ----warning=FALSE------------------------------------------------------------
# Summarize predictor in high sens region
summarize_predictor(
  data = iris_subset,
  predictor = Sepal.Length,
  response = Species,
  threshold = 0.9,
  ratio = "tpr"
)

# Summarize predictor in high spec region
summarize_predictor(
  data = iris_subset,
  predictor = Sepal.Length,
  response = Species,
  threshold = 0.1,
  ratio = "fpr"
)

## ----warning=FALSE------------------------------------------------------------
summarize_dataset(
  data = iris_subset,
  response = Species,
  threshold = 0.9,
  ratio = "tpr"
)

## ----warning=FALSE------------------------------------------------------------
# Plot ROC points of Sepal.Length
sepal_length_plot <- plot_roc_points(
  data = iris_subset,
  predictor = Sepal.Length,
  response = Species
)
sepal_length_plot

## ----warning=FALSE------------------------------------------------------------
sepal_length_plot +
  add_roc_curve(
    data = iris_subset,
    predictor = Sepal.Width,
    response = Species
  ) +
  add_roc_points(
    data = iris_subset,
    predictor = Petal.Width,
    response = Species
  ) +
  add_partial_roc_curve(
    data = iris_subset,
    predictor = Petal.Length,
    response = Species,
    ratio = "tpr",
    threshold = 0.7
  ) +
  add_threshold_line(
    threshold = 0.7,
    ratio = "tpr"
  ) +
  add_chance_line()