gofRRMSE: Relative Root Mean Square Error

In ehaGoF: Calculates Goodness of Fit Statistics

Description

Calculates and returns relative root mean square error (RRMSE) of the model. The ratio of the mean of square root of residuals squared to the mean of observed values.

Usage

gofRRMSE(Obs, Prd, dgt = 3)

Arguments

Obs	Observed values or target vector.
Prd	Predicted values. Values produced by approximation or regression.
dgt	Number of digits in decimal places. Default is 3.

Details

RRMSE is calculated by dividing RMSE by the mean of observed values.

Value

RelativeRootMeanSquareError

Relative root mean square error (RRMSE) of given set.

Note

For more information: Despotovic, M., Nedic, V., Despotovic, D., & Cvetanovic, S., Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation, Renewable and Sustainable Energy Reviews, Volume 56, April 2016, Pages 246-260, ISSN 1364-0321, http://dx.doi.org/10.1016/j.rser.2015.11.058.

Author(s)

Prof. Dr. Ecevit Eyduran, TA. Alper Gulbe

References

Comparison of the Predictive Capabilities of Several Data Mining Algorithms and Multiple Linear Regression in the Prediction of Body Weight by Means of Body Measurements in the Indigenous Beetal Goat of Pakistan, Ecevit Eyduran, Daniel Zaborski, Abdul Waheed, Senol Celik, Koksal Karadas and Wilhelm Grzesiak, Pakistan J. Zool., vol. 49(1), pp 257-265, 2017.

Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data - Precious Nokuthula Wistebaar Mahlangu, Renaud Mathieu, Konrad Wessels, Laven Naidoo, Michel M Verstraete, Gregory P Asner, Russell Main

Examples

# Input values, independent variable
input <- 0:4

# Target vector, observed values, dependent variable
target <- c(1.9, 4.1, 5.89, 7.9, 10.01)

# Simple linear regression, target across input like: target = a * input + b,
# where a and b are coefficients.
model <- lm(target~input)

# Information about the model
summary(model)

# Values predicted by the model
predicted <- predict(model)

# using library ehaGoF for goodness of fit
library(ehaGoF)

# Goodness of fit - relative root mean square error (RRMSE)
gofRRMSE(target, predicted)

Example output

Call:
lm(formula = target ~ input)

Residuals:
     1      2      3      4      5 
-0.056  0.142 -0.070 -0.062  0.046 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.95600    0.08266   23.66 0.000165 ***
input        2.00200    0.03374   59.33 1.05e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1067 on 3 degrees of freedom
Multiple R-squared:  0.9991,	Adjusted R-squared:  0.9989 
F-statistic:  3520 on 1 and 3 DF,  p-value: 1.055e-05

[1] 1.387

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