从GSL库中的C gsl_fit_linear()函数中获取线性回归的p值 [英] Getting p-value for linear regression in C gsl_fit_linear() function from GSL library
问题描述
我正在尝试从C中的R重新获得一些代码，所以我正在尝试使用gsl_fit_linear()函数来拟合线性回归.
I'm trying to reporduce some code from R in C, so I'm trying to fit a linear regression using the gsl_fit_linear() function.
在R中，我将使用lm()函数，该函数使用以下代码返回拟合的p值:
In R I'd use the lm() function, which returns a p-value for the fit using this code:
lmAvgs<- lm( c(1.23, 11.432, 14.653, 21.6534) ~ c(1970, 1980, 1990, 2000) )
summary(lmAvgs)
我不知道如何从C输出转换为p值，到目前为止，我的代码看起来像这样:
I've no idea though how to go from the C output to a p-value, my code looks something like this so far:
int main(void)
{
int i, n = 4;
double x[4] = { 1970, 1980, 1990, 2000 };
double y[4] = {1.23, 11.432, 14.653, 21.6534};
double c0, c1, cov00, cov01, cov11, sumsq;
gsl_fit_linear (x, 1, y, 1, n, &c0, &c1, &cov00, &cov01, &cov11, &sumsq);
}
这似乎可以正确计算斜率和截距，但我不知道如何获取p值.我是stats和C的新手！
This seems to correctly calculate slope and intercept but I don't know how to get a p-value. I'm novice at stats and C!
推荐答案
一切就绪: http://en.wikipedia.org /wiki/Ordinary_least_squares .但是这是一段代码，显示的输出类似于R中的summary(lmAvgs).要运行此代码，您需要 GSL库:
Everything is on : http://en.wikipedia.org/wiki/Ordinary_least_squares. but here is a piece of code which display an output similar to summary(lmAvgs) in R. To run this, you need the GSL Library :
int n = 4;
double x[4] = { 1970, 1980, 1990, 2000};
double y[4] = {1.23, 11.432, 14.653, 21.6534};
double c0, c1, cov00, cov01, cov11, sumsq;
gsl_fit_linear (x, 1, y, 1, n, &c0, &c1, &cov00, &cov01, &cov11, &sumsq);
cout<<"Coefficients\tEstimate\tStd. Error\tt value\tPr(>|t|)"<<endl;
double stdev0=sqrt(cov00);
double t0=c0/stdev0;
double pv0=t0<0?2*(1-gsl_cdf_tdist_P(-t0,n-2)):2*(1-gsl_cdf_tdist_P(t0,n-2));//This is the p-value of the constant term
cout<<"Intercept\t"<<c0<<"\t"<<stdev0<<"\t"<<t0<<"\t"<<pv0<<endl;
double stdev1=sqrt(cov11);
double t1=c1/stdev1;
double pv1=t1<0?2*(1-gsl_cdf_tdist_P(-t1,n-2)):2*(1-gsl_cdf_tdist_P(t1,n-2));//This is the p-value of the linear term
cout<<"x\t"<<c1<<"\t"<<stdev1<<"\t"<<t1<<"\t"<<pv1<<endl;
double dl=n-2;//degrees of liberty
double ym=0.25*(y[0]+y[1]+y[2]+y[3]); //Average of vector y
double sct=pow(y[0]-ym,2)+pow(y[1]-ym,2)+pow(y[2]-ym,2)+pow(y[3]-ym,2); // sct = sum of total squares
double R2=1-sumsq/sct;
cout<<"Multiple R-squared: "<<R2<<", Adjusted R-squared: "<<1-double(n-1)/dl*(1-R2)<<endl;
double F=R2*dl/(1-R2);
double p_value=1-gsl_cdf_fdist_P(F,1,dl);
cout<<"F-statistic: "<<F<<" on 1 and "<<n-2<<" DF, p-value: "<<p_value<<endl;
哪个给:
Coefficients Estimate Std. Error t value Pr(>|t|)
Intercept -1267.91 181.409 -6.98922 0.0198633
x 0.644912 0.0913886 7.05681 0.0194956
Multiple R-squared: 0.961389, Adjusted R-squared: 0.942083
F-statistic: 49.7986 on 1 and 2 DF, p-value: 0.0194956
R给出:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.268e+03 1.814e+02 -6.989 0.0199 *
c(1970, 1980, 1990, 2000) 6.449e-01 9.139e-02 7.057 0.0195 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.044 on 2 degrees of freedom
Multiple R-squared: 0.9614, Adjusted R-squared: 0.9421
F-statistic: 49.8 on 1 and 2 DF, p-value: 0.01950
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