nda (version 0.2.4)

predict.nda: Calculation of predicted values of Generalized Network-based Dimensionality Reduction and Analysis (GNDA)

Description

Calculation of predicted values of Generalized Network-based Dimensionality Reduction and Analysis (GNDA)

Usage

# S3 method for nda
predict(object, newdata, ...)

Value

Residual values (data frame)

Arguments

object

An object of class 'nda'.

newdata

A required data frame in which to look for variables with which to predict.

...

further arguments passed to or from other methods.

Author

Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona

e-mail*: [e-mail]

References

Kosztyán, Z. T., Katona, A. I., Kurbucz, M. T., & Lantos, Z. (2024). Generalized network-based dimensionality analysis. Expert Systems with Applications, 238, 121779. <URL: https://doi.org/10.1016/j.eswa.2023.121779>.

See Also

plot, print, ndr.

Examples

# Example of prediction function of GNDA
set.seed(1) # Fix the random seed
data(swiss) # Use Swiss dataset
resdata<-swiss
sample <- sample(c(TRUE, FALSE), nrow(resdata), replace=TRUE, prob=c(0.9,0.1))
train <- resdata[sample, ] # Split the dataset to train and test
test <- resdata[!sample, ]
p<-ndr(train) # Use GNDA only on the train dataset
P<-ndr(swiss) # USE GNDA on the entire dataset
res<-predict(p,test) # Calculate the prediction to the test dataset
real<-P$scores[!sample, ]
cor(real,res) # The correlation between original and predicted values