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.

An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

Jin Li

spm2

Spatial Predictive Modeling

ccr function

<dl><dt>obs</dt>
<dd>a vector of observation values of validation samples.</dd>
<dt>pred</dt>
<dd>a vector of prediction values of predictive models for validation samples.</dd></dl>

Arguments

Author

Correct classification rate for predictive models based on cross
-validation — ccr

<dl>

<dt>obs</dt>
<dd>a vector of observation values of validation samples.</dd>


<dt>pred</dt>
<dd>a vector of prediction values of predictive models for validation samples.</dd>

</dl>

ccr: Correct classification rate for predictive models based on cross -validation

Description

Usage

Value

Arguments

Author

References

Examples