Goodness of Fit - Pearson's Correlation Coefficients {ehaGoF} | R Documentation |
Pearson's Correlation Coefficients
Description
Calculates and returns Pearson's correlation coefficients (PC).
Usage
gofPC(Obs, Prd, 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. |
dgt |
Number of digits in decimal places. Default is 3. |
Value
PearsonCorrelation |
Pearson's correlation coefficients (PC) |
Author(s)
Prof. Dr. Ecevit EYDURAN, TA. Alper GULBE
References
OBILOR Esezi Isaac, AMADI Eric Chikweru, Test for Significance of Pearson’s Correlation Coefficient, International Journal of Innovative Mathematics, Statistics & Energy Policies 6(1):11-23, Jan-Mar, 2018.
Reza Soleimani, Amir Hossein Saeedi Dehaghani, Alireza Bahadori, A new decision tree based algorithm for prediction of hydrogen sulfide solubility in various ionic liquids, Journal of Molecular Liquids, Volume 242, 2017, Pages 701-713, ISSN 0167-7322, https://doi.org/10.1016/j.molliq.2017.07.075. (http://www.sciencedirect.com/science/article/pii/S0167732217305123)
Examples
# 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)
# model's predicted values against targets
predicted<-model$fitted.values
# using library ehaGoF for goodness of fit.
library(ehaGoF)
# Goodness of fit - Pearson's correlation coefficient
gofPC(targets, predicted)