Goodness of Fit - Pearson's Correlation Coefficients {ehaGoF}

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)