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)
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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.
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)
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