ccr: Correct classification rate for predictive models based on...
| ccr | R Documentation |
Correct classification rate for predictive models based on cross -validation
Description
This function is to calculates correct classification rate (ccr) for categorical data with the observed (obs) data specified as factor. It based on the differences between the predicted values for and the observed values of validation samples for cross-validation. For 0 and 1 data, the observed values need to be specified as factor in order to use this accuracy measure. It is modified from the function 'pred.acc' in 'spm' package.
Usage
ccr(obs, pred)
Arguments
obs |
a vector of observation values of validation samples. |
pred |
a vector of prediction values of predictive models for validation samples. |
Value
A list with the following component: ccr (correct classification rate) for categorical data.
Author(s)
Jin Li
References
Jin Li (2019). spm: Spatial Predictive Modeling. R package version 1.2.0. https://CRAN.R-project.org/package=spm.
Examples
set.seed(1234)
x <- as.factor(sample(letters[1:2], 30, TRUE))
y <- sample(x, 30)
ccr(x, y)
