grover11

05-04-2009, 02:10 PM

Hi,

I am using JMSL 5.0.1 on WinXP with SP2 and JRE 1.6.0_06. I am using the NonlinearRegression class to generate the coefficients a and b in the equation Y = a * e^(b * x). I generated the initial parameters values for these coefficients with some sample data, and provided them to the setGuess(), where a = 18.755497533160803 and b = 0.00022960312118070167.

I used the initial parameter values above with nls() in R, which by default uses a Gauss-Newton algorithm, and it generated the coefficients for the exponential equation as a = 850.7 and b = 0.0005322.

In addition, I used MS Excel to create a scatterplot with an exponential trendline. The exponential equation has a = 854.06 and c = 0.0005. I?m not sure what algorithm Excel is using to generate these coefficients.

When I generate the coefficients using JMSL, solve() returns a= 71.84244133504096 and b = 0.0020465827799000125. I am puzzled as to why the coefficients for a and b generated by JMSL differ from those generated by R and Excel. Would someone please let me know what is wrong with my implementation below?

My sample data is attached; it is a CSV file with the extension txt.

Thank you.

import java.io.BufferedReader;

import java.io.File;

import java.io.FileReader;

import java.util.Vector;

import com.imsl.stat.NonlinearRegression;

public class ExponentialCoefficients {

private File csvFile;

private double[] xinput;

private double[] yinput;

private int nobs;

private double estimated_a;

private double estimated_b;

//calculate coefficients a and b in predicted y = a * e^(b * x)

public static void main(String[] args){

try{

ExponentialCoefficients exp = new ExponentialCoefficients();

exp.calculateCoefficients();

}

catch(Exception e){

e.printStackTrace();

}

}

public ExponentialCoefficients() throws Exception{

csvFile = new File("<File path>\\xy_values.txt");

parseCSVLines(readTextFileLines(csvFile));

nobs = xinput.length;

estimated_a = 18.755497533160803;

estimated_b = 0.00022960312118070167;

}

public void calculateCoefficients() throws Exception{

int nparam = 2;

double theta[] = new double[2];

theta[0] = estimated_a;

theta[1] = estimated_b;

NonlinearRegression regression = new NonlinearRegression(nparam);

regression.setGuess(theta);

double coef[] = regression.solve(f);

System.out.println("The computed regression coefficients are {" +

coef[0] + ", " + coef[1] + "}");

}

NonlinearRegression.Function f = new NonlinearRegression.Function() {

public boolean f(double theta[], int iobs, double frq[],

double wt[], double e[]){

boolean iend;

if(iobs < nobs){

wt[0] = 1.0;

frq[0] = 1.0;

iend = true;

//System.out.println("theta[0]: "+theta[0] + "\t theta[1]: " + theta[1]);

double predictedY = theta[0] * Math.exp(theta[1] * xinput[iobs]);

e[0] = yinput[iobs] - predictedY;

} else {

iend = false;

}

return iend;

}

};

//Read in lines from Text file and load lines (rows) into vector.

public Vector<String> readTextFileLines(File textFile)throws Exception {

Vector<String> csvLines = new Vector<String>();

//read file

BufferedReader reader = new BufferedReader(new FileReader(textFile));

//read in each line

String line = null;

while( (line = reader.readLine()) != null ){

csvLines.add(line);

}

reader.close();

return csvLines;

}

//Parse lines into individual values and store them.

public void parseCSVLines(Vector<String> csvLines)throws Exception{

// remove header row

csvLines.remove(0);

//initialize double[][]

xinput = new double[csvLines.size()];

yinput = new double[csvLines.size()];

//populate double[][] with values

for(int index = 0; index < csvLines.size(); index++){

String row = csvLines.get(index);

String[] xyvalues = row.split(",");

xinput[index] = Double.parseDouble(xyvalues[0]);

yinput[index] = Double.parseDouble(xyvalues[1]);

}

}

}//end

I am using JMSL 5.0.1 on WinXP with SP2 and JRE 1.6.0_06. I am using the NonlinearRegression class to generate the coefficients a and b in the equation Y = a * e^(b * x). I generated the initial parameters values for these coefficients with some sample data, and provided them to the setGuess(), where a = 18.755497533160803 and b = 0.00022960312118070167.

I used the initial parameter values above with nls() in R, which by default uses a Gauss-Newton algorithm, and it generated the coefficients for the exponential equation as a = 850.7 and b = 0.0005322.

In addition, I used MS Excel to create a scatterplot with an exponential trendline. The exponential equation has a = 854.06 and c = 0.0005. I?m not sure what algorithm Excel is using to generate these coefficients.

When I generate the coefficients using JMSL, solve() returns a= 71.84244133504096 and b = 0.0020465827799000125. I am puzzled as to why the coefficients for a and b generated by JMSL differ from those generated by R and Excel. Would someone please let me know what is wrong with my implementation below?

My sample data is attached; it is a CSV file with the extension txt.

Thank you.

import java.io.BufferedReader;

import java.io.File;

import java.io.FileReader;

import java.util.Vector;

import com.imsl.stat.NonlinearRegression;

public class ExponentialCoefficients {

private File csvFile;

private double[] xinput;

private double[] yinput;

private int nobs;

private double estimated_a;

private double estimated_b;

//calculate coefficients a and b in predicted y = a * e^(b * x)

public static void main(String[] args){

try{

ExponentialCoefficients exp = new ExponentialCoefficients();

exp.calculateCoefficients();

}

catch(Exception e){

e.printStackTrace();

}

}

public ExponentialCoefficients() throws Exception{

csvFile = new File("<File path>\\xy_values.txt");

parseCSVLines(readTextFileLines(csvFile));

nobs = xinput.length;

estimated_a = 18.755497533160803;

estimated_b = 0.00022960312118070167;

}

public void calculateCoefficients() throws Exception{

int nparam = 2;

double theta[] = new double[2];

theta[0] = estimated_a;

theta[1] = estimated_b;

NonlinearRegression regression = new NonlinearRegression(nparam);

regression.setGuess(theta);

double coef[] = regression.solve(f);

System.out.println("The computed regression coefficients are {" +

coef[0] + ", " + coef[1] + "}");

}

NonlinearRegression.Function f = new NonlinearRegression.Function() {

public boolean f(double theta[], int iobs, double frq[],

double wt[], double e[]){

boolean iend;

if(iobs < nobs){

wt[0] = 1.0;

frq[0] = 1.0;

iend = true;

//System.out.println("theta[0]: "+theta[0] + "\t theta[1]: " + theta[1]);

double predictedY = theta[0] * Math.exp(theta[1] * xinput[iobs]);

e[0] = yinput[iobs] - predictedY;

} else {

iend = false;

}

return iend;

}

};

//Read in lines from Text file and load lines (rows) into vector.

public Vector<String> readTextFileLines(File textFile)throws Exception {

Vector<String> csvLines = new Vector<String>();

//read file

BufferedReader reader = new BufferedReader(new FileReader(textFile));

//read in each line

String line = null;

while( (line = reader.readLine()) != null ){

csvLines.add(line);

}

reader.close();

return csvLines;

}

//Parse lines into individual values and store them.

public void parseCSVLines(Vector<String> csvLines)throws Exception{

// remove header row

csvLines.remove(0);

//initialize double[][]

xinput = new double[csvLines.size()];

yinput = new double[csvLines.size()];

//populate double[][] with values

for(int index = 0; index < csvLines.size(); index++){

String row = csvLines.get(index);

String[] xyvalues = row.split(",");

xinput[index] = Double.parseDouble(xyvalues[0]);

yinput[index] = Double.parseDouble(xyvalues[1]);

}

}

}//end