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

Usage

 1 gofACoD(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.

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 19 inputs <- 0:19 # dummy targets/observed values, dependent variable # a product of 2*times inputs minus 5 with some normal noise targets <- -5 + inputs*1.2 + rnorm(20) # 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 coefficient of determination (adjusted R-squared) gofACoD(targets, predicted, dgt=4,nTermInAppr=n)

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