如何绘制R中的线性回归?-howtoplotthelinearregressioninR?

7  

Are you looking for the predict function?

你在寻找预测函数吗?

E.g.: using lines(predict(fit)) will give:

使用线(预测(拟合))将给出:

You could also use this for predicting future data aligning with the calculated coefficients. E.g.

你也可以用它来预测未来的数据与计算的系数对齐。如。

# plot the existing data with space for the predicted line
plot(c(cpi,rep(NA,12)),xaxt="n",ylab="CPI",xlab="",ylim=c(162,190))

# plot the future predictions as a line using the next 3 year periods
lines(13:24,
      predict(
        fit,
        newdata=data.frame(year=rep(c(2011,2012,2013),each=4),quarter=rep(1:4,3))
             )
     )

year<-rep(2008:2013,each=4)
axis(1,labels=paste(year,quarter,sep="C"),at=1:24,las=3)

6  

cpi<-c(162.2,164.6,166.5,166.0,166.4,167.0,168.6,169.5,170.0,172.0,173.3,174.0)
cpits <- ts(cpi, start=2008, frequency=4)
plot(decompose(cpits))

6  

Humbug. These are all reasonable solutions, but they don't do what you ask for. Now what you ask for is slightly cooler and completely impractical, but can be done using rgl.

欺骗。这些都是合理的解决方案，但它们并不能满足你的要求。现在你要求的是稍微冷却，完全不实用，但是可以使用rgl。

f <- function(x, y, coefs){
  z <- coefs[1] + coefs[2] * x + coefs[3] * y
  z
}

x <- seq(from=min(year), to=max(year), length.out=100)
y <- seq(from=min(quarter), to=max(quarter), length.out=100)

z <- outer(x, y, f, coefs=coef(fit))

Now where the magic happens in rgl:

现在魔术发生在rgl:

library(rgl)

persp3d(x, y, z, col="lightblue")

It isn't done justice here, but it's pretty and you can move it about.

这里没有公正，但它很漂亮，你可以移动它。

And what the hell, let's add your original points

让我们添加你的原始点。

points3d(year, quarter, cpi, size=5, col="red")

2  

The error lies in the way you're data was formatted. Here is another option:

错误在于数据被格式化的方式。这是另一个选择:

year<-seq(from=2008,to=2010.75,by=.25)
cpi<-c(162.2,164.6,166.5,166.0,166.4,167.0,168.6,169.5,170.0,172.0,173.3,174.0)
df <- data.frame(year,cpi)
plot(df)+abline(lm(df$cpi~df$year))

Then you can reformat the axes labels if you like.

然后，如果您愿意，可以重新格式化坐标轴标签。

1  

The Predict.Plot and TkPredict functions in the TeachingDemos package will plot the relationship between one of the predictors and the response variable conditioned on the values of the other predictors. Predict.Plot makes it fairly simple to see multiple lines from different conditions while TkPredict lets you interactively change the values being conditioned on (and will produce the Predict.Plot code to recreate the current plot).

的预测。在教学演示程序包中，Plot和TkPredict函数将绘制一个预测器与响应变量之间的关系，该变量的条件是其他预测器的值。预测。Plot使得从不同的条件中看到多个行变得相当简单，而TkPredict则允许您交互地更改受条件约束的值(并将生成预测)。绘制当前地块的地块代码。

These functions are general for regression models on multiple predictors, but will not be as good as decompose for a time series.

这些函数一般用于多个预测器的回归模型，但不会像分解时间序列那样好。