gofARSq: Adjusted R-Squared (Adjusted Coefficient of Determination)
In ehaGoF: Calculates Goodness of Fit Statistics

Description Usage Arguments Value Author(s) References Examples

Caclulates and returns adjusted coefficient of determination (adjusted R-squared).

1	gofARSq(Obs, Prd, nTermInAppr = 2, dgt = 3)

`Obs`	Observed or measured values or target vector.
`Prd`	Predicted or fitted values by the model. Values produced by approximation or regression.
`nTermInAppr`	Number of terms in approximation or regression models formula, interception included. For simple linear regression with one independent variable is simply 2. Default is 2.
`dgt`	Number of digits in decimal places. Default is 3.

ARsquared

Goodness of fit - adjusted coefficient of determination (adjusted R-squared)

Prof. Dr. Ecevit Eyduran, TA. Alper Gulbe

Comparison of Different Data Mining Algorithms for Prediction of Body Weight From Several Morphological Measurements in Dogs - S Celik, O Yilmaz.

A new decision tree based algorithm for prediction of hydrogen sulfide solubility in various ionic liquids - Reza Soleimani, Amir Hossein Saeedi Dehaghani, Alireza Bahadori.

# dummy inputs, independent variable
# integers from 0 to 99
inputs <- 0:99

# dummy targets/observed values, dependent variable
# a product of 2*times inputs minus 5 with some normal noise
targets <- -5 + inputs*1.2 + rnorm(100)

# linear regression model
model<-lm(targets~inputs)

# About the model
summary(model)

# Number of Terms
n = length(model$coefficients)

# model's predicted values against targets
predicted<-model$fitted.values

# using library ehaGoF for goodness of fit.
library(ehaGoF)

# Goodness of fit : adjusted R-squared
gofARSq(targets, predicted, dgt=4, nTermInAppr=n)