R中具有已知固定截距的线性回归 [英] Linear Regression with a known fixed intercept in R

问题描述

我想使用R中的lm()函数来计算线性回归.此外，我想获得回归的斜率，在此我将截距明确地赋予lm().

I want to calculate a linear regression using the lm() function in R. Additionally I want to get the slope of a regression, where I explicitly give the intercept to lm().

我在互联网上找到一个示例，尝试读取R帮助?lm"(不幸的是我听不懂)，但没有成功.谁能告诉我我的错误在哪里?

I found an example on the internet and I tried to read the R-help "?lm" (unfortunately I'm not able to understand it), but I did not succeed. Can anyone tell me where my mistake is?

lin <- data.frame(x = c(0:6), y = c(0.3, 0.1, 0.9, 3.1, 5, 4.9, 6.2))
plot (lin$x, lin$y)

regImp = lm(formula = lin$x ~ lin$y)
abline(regImp, col="blue")

# Does not work:
# Use 1 as intercept
explicitIntercept = rep(1, length(lin$x))
regExp = lm(formula = lin$x ~ lin$y + explicitIntercept)
abline(regExp, col="green")

感谢您的帮助.

推荐答案

您可以从回归中减去显式截距，然后拟合无截距模型:

You could subtract the explicit intercept from the regressand and then fit the intercept-free model:

> intercept <- 1.0
> fit <- lm(I(x - intercept) ~ 0 + y, lin)
> summary(fit)

0 +抑制lm对截距的拟合.

编辑，要绘制拟合，请使用

edit To plot the fit, use

> abline(intercept, coef(fit))

P.S.模型中的变量看起来是错误的:通常是y ~ x，而不是x ~ y(即，回归变量应在左侧，而回归变量应在右侧).

P.S. The variables in your model look the wrong way round: it's usually y ~ x, not x ~ y (i.e. the regressand should go on the left and the regressor(s) on the right).

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