CODEWITHSUNDEEP
rlinear-regression
Little
Little

Reputation: 3477

how to plot the linear regression in R?

I want to make the following case of linear regression in R

year<-rep(2008:2010,each=4)
quarter<-rep(1:4,3)
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)
plot(cpi,xaxt="n",ylab="CPI",xlab="")
axis(1,labels=paste(year,quarter,sep="C"),at=1:12,las=3)
fit<-lm(cpi~year+quarter)

I want to plot the line that shows the linear regression of the data that I process. I have tried with:

abline(fit)
abline(fit$coefficients[[1]],c(fit$coefficients[[2]],fit$coefficients[[3]]))

The problem is that my formula is of the form:

y=a+b*year+c*quarter

and not something simpler like:

y=a+b*year

so how I can draw that line that shows the linear regression?

Is it possible to draw the line with abline?

Upvotes: 10

Views: 25251

Answers (5)

Greg Snow
Greg Snow

Reputation: 49640

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).

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

Upvotes: 1

N8TRO
N8TRO

Reputation: 3364

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

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))

enter image description here

Then you can reformat the axes labels if you like.

Upvotes: 3

sebastian-c
sebastian-c

Reputation: 15395

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.

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:

library(rgl)

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

enter image description here

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

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

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

enter image description here

Upvotes: 6

thelatemail
thelatemail

Reputation: 93813

Are you looking for the predict function?

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

enter image description here

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

# 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)

enter image description here

Upvotes: 8

Matthew Lundberg
Matthew Lundberg

Reputation: 42649

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))

enter image description here

Upvotes: 6

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