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

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```# 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) ```

ehaGoF documentation built on Aug. 11, 2020, 5:08 p.m.