erroR {gofcat} | R Documentation |
Performance metrics for categorical models
Description
Calculates common error metrics of fitted binary and multi-categorical response models. Available measures include: the brier score, logloss and misclassification error.
Usage
erroR(model, type = c("brier", "logloss", "misclass"), thresh = 5e-1)
Arguments
model |
a model object or data.frame of observed and predicted values.
The following class of objects can be directly passed to the |
type |
specifies the type of error metrics |
thresh |
resets the default misclassification threshold |
Value
value |
a numeric vector of the realized error value. |
type |
a character vector of error type. |
threshold |
a numeric vector of the misclassification threshold. |
See Also
Examples
require(serp)
set.seed(1)
n <- 100
y <- factor(rbinom(n, 1, 0.3))
x <- rnorm(n)
#p <- runif(n)
m1 <- glm(y ~ x, family = binomial())
erroR(m1, type = "brier")
erroR(m1, type = "logloss")
erroR(m1, type = "misclass")
erroR(m1, type = "misclass", thresh=0.3)
# using data.frame
df <- data.frame(y, fitted(m1))
erroR(df, type = "brier")
m2 <- serp(rating ~ temp + contact, slope = "parallel", link = "logit",
data = wine)
erroR(m2, type = "brier")
erroR(m2, type = "logloss")
erroR(m2, type = "misclass")
[Package gofcat version 0.1.2 Index]