spm (version 1.2.2)

pred.acc: Predictive error and accuracy measures for predictive models based on cross-validation

Description

This function is used to calculate the mean error (me), mean absolute error (mae), mean squared error (mse), relative me (rme), relative mae (rmae), root mse (rmse), relative rmse (rrmse), variance explained by predictive models based on cross-validation (vecv), and Legates and McCabe's E1 (e1) for numerical data; and it also calculates correct classification rate (ccr), kappa (kappa), sensitivity (sens), specificity (spec), and true skill statistic (tss) for categorical data with the observed (obs) data specified as factor. They are 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 accuracy measures for categorical data. Moreover, sens, spec, tss and rmse are for categorical data with two levels (e.g. presence and absence data).

Usage

pred.acc(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 components: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1 for numerical data; ccr, kappa, sens, spec and tss for categorical data with two levels; and ccr, kappa for categorical data with more than two levels.

References

Li, J., 2016. Assessing spatial predictive models in the environmental sciences: accuracy measures, data variation and variance explained. Environmental Modelling & Software 80 1-8.

Li, J., 2017. Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? PLOS ONE 12 (8): e0183250.

Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and true skill statistic (TSS). Journal of Applied Ecology 43 1223-1232.

Examples

# NOT RUN {
set.seed(1234)
x <- sample(1:30, 30)
e <- rnorm(30, 1)
y <- x + e
pred.acc(x, y)

y <- 0.8 * x + e
pred.acc(x, y)

# }