calc_tpr: Measure model performance

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

Description

This function evaluates a binary classification model. The function calculates true positive rate, false positive rate, true negative rate, false negative rate, and positive predictive value. These values are added to the data-frame provided in 'data'.

Usage

calc_tpr(pred_values, pos_pred, pos)

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
 measure_perf(predictor = .fitted, known_class = outcome)

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