Linear/Nonlinear Regression fitting example using R language (1) _ Data analysis

A linear/Nonlinear regression fitting example using R language (1)

1. Generate a set of data

vector<float>xxvec;

vector<float>yyvec;

Ofstreamfout ("Data2.txt");

for (int i =1;i<200;i++)

{

float x =i*0.8;

Float randdnum= rand ()%10 * 10;

Floatrandomflag = (rand ()%10)%2==0? (1):(-1);

Float y = 3 *x*x + 2*x + 5 + randomflag*randdnum;

fout<<x<< "" <<y<<endl;

Xxvec.push_back (x);

Yyvec.push_back (y);

}

Fout.close ();

Save the generated data as a TXT file, named "Data1"

2. Linear Fitting

#-------------------------------------------------------------#载入数据

> Fire <-read.table (' d:/data.txt ', header = TRUE)

#-------------------------------------------------------------#回归分析

> Plot (fire$y ~ fire$x)

> Fire.reg <-lm (fire$y ~ fire$x,data = fire) #回归拟合

> Data1.reg

Call:

LM (formula = Data1$y ~ data1$x, data = data1)

Coefficients:

(Intercept) data1$x

6.202 3.003

>summary (Data1.reg) #回归分析表

Call:

LM (formula = Data1$y ~ data1$x, data = data1)

Residuals:

Min 1Q Median 3Q Max

-93.345-42.929-1.948 46.717 88.793

Coefficients:

Estimate Std. Error TValue Pr (>|t|)

(Intercept) 6.202084 3.352055 1.85 0.0646.

data1$x 3.002826 0.007259 413.66 <2e-16 * * *

The red number is the linear regression coefficient, because the data generated by adding a random number, so the line to fit it is:

y=3.002826 x+6.202084

---

Signif. codes:0 ' * * * 0.001 ' * * ' 0.01 ' * ' 0.05 '. ' 0.1 "' 1

Residual standard error:52.93 on 997 degrees of freedom

Multiple r-squared:0.9942, adjusted r-squared:0.9942

F-statistic:1.711e+05 on 1 and 997 DF, P-value: < 2.2e-16

>anova (Data1.reg) #方差分析表

Analysis of Variance Table

Response:data1$y

Df Sum Sq Mean sq F value Pr (>f)

data1$x 1479462873 479462873 171112 < 2.2e-16***

Residuals 997 2793641 2802

Signif. codes:0 ' * * * 0.001 ' * * ' 0.01 ' * ' 0.05 '. ' 0.1 "' 1

>abline (Data1.reg, col = 2, lty = 2) #拟合直线