make_roc: Receiver operating characteristic (ROC) curve

In dariyasydykova/tidyroc: Calculates ROC and precision-recall curves

Description

This function calculates true positive rate and false positive rate to plot an ROC curve.

Usage

make_roc(data, ...)

Arguments

data	data-frame that contains fitted values and known outcomes
predictor	column in 'data' that contains fitted values
known_class	column in 'data' that contains true or actual classification

Examples

library(tidyverse)
library(broom)
library(tidyroc)

# get `biopsy` dataset from `MASS`
data(biopsy, package = "MASS")

# change column names from `V1`, `V2`, etc. to informative variable names
colnames(biopsy) <-
  c(
    "ID",
    "clump_thickness",
    "uniform_cell_size",
    "uniform_cell_shape",
    "marg_adhesion",
    "epithelial_cell_size",
    "bare_nuclei",
    "bland_chromatin",
    "normal_nucleoli",
    "mitoses",
    "outcome"
  )

# fit a logistic regression model to predict tumour type
glm(outcome ~ clump_thickness + uniform_cell_shape,
  family = binomial,
  data = biopsy
) %>%
  augment() %>% # use broom to add glm output to the original data frame
  make_roc(predictor = .fitted, known_class = outcome) %>% # get values to plot an ROC curve
  ggplot(aes(x = fpr, y = tpr)) + # plot false positive rate against true positive rate
  geom_line()

dariyasydykova/tidyroc documentation built on May 14, 2019, 11:03 p.m.