Goodness of Fit : Adjusted R-Squared {ehaGoF}

Adjusted R-Squared (Adjusted Coefficient of Determination)

Description

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

Usage

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

Arguments

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.

Value

ARsquared

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

Author(s)

Prof. Dr. Ecevit Eyduran, TA. Alper Gulbe

References

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.

Examples

# 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)