CODEWITHSUNDEEP
rplotregressionlinear-regressionlm
Oposum
Oposum

Reputation: 1243

Produce nice linear regression plot (fitted line, confidence / prediction bands, etc)

I have this sample 10-year regression in the future.

date<-as.Date(c("2015-12-31", "2014-12-31", "2013-12-31", "2012-12-31"))
value<-c(16348, 14136, 12733, 10737)
#fit linear regression
model<-lm(value~date)
#build predict dataframe
dfuture<-data.frame(date=seq(as.Date("2016-12-31"), by="1 year", length.out = 10))
#predict the futurne
predict(model, dfuture, interval = "prediction")

How can I add confidence bands to this?

Upvotes: 2

Views: 5438

Answers (2)

Zheyuan Li
Zheyuan Li

Reputation: 73385

The following code will generate good-looking regression plot for you. My comments along the code should explain everything clear. The code will use value, model as in your question.

## all date you are interested in, 4 years with observations, 10 years for prediction
all_date <- seq(as.Date("2012-12-31"), by="1 year", length.out = 14)

## compute confidence bands (for all data)
pred.c <- predict(model, data.frame(date=all_date), interval="confidence")

## compute prediction bands (for new data only)
pred.p <- predict(model, data.frame(date=all_date[5:14]), interval="prediction")

## set up regression plot (plot nothing here; only set up range, axis)
ylim <- range(range(pred.c[,-1]), range(pred.p[,-1]))
plot(1:nrow(pred.c), numeric(nrow(pred.c)), col = "white", ylim = ylim,
     xaxt = "n", xlab = "Date", ylab = "prediction",
     main = "Regression Plot")
axis(1, at = 1:nrow(pred.c), labels = all_date)

## shade 95%-level confidence region
polygon(c(1:nrow(pred.c),nrow(pred.c):1), c(pred.c[, 2], rev(pred.c[, 3])),
        col = "grey", border = NA)

## plot fitted values / lines
lines(1:nrow(pred.c), pred.c[, 1], lwd = 2, col = 4)

## add 95%-level confidence bands
lines(1:nrow(pred.c), pred.c[, 2], col = 2, lty = 2, lwd = 2)
lines(1:nrow(pred.c), pred.c[, 3], col = 2, lty = 2, lwd = 2)

## add 95%-level prediction bands
lines(4 + 1:nrow(pred.p), pred.p[, 2], col = 3, lty = 3, lwd = 2)
lines(4 + 1:nrow(pred.p), pred.p[, 3], col = 3, lty = 3, lwd = 2)

## add original observations on the plot
points(1:4, rev(value), pch = 20)

## finally, we add legend
legend(x = "topleft", legend = c("Obs", "Fitted", "95%-CI", "95%-PI"),
       pch = c(20, NA, NA, NA), lty = c(NA, 1, 2, 3), col = c(1, 4, 2, 3),
       text.col = c(1, 4, 2, 3), bty = "n")

regression plot

The JPEG is generated by code:

jpeg("regression.jpeg", height = 500, width = 600, quality = 100)
## the above code
dev.off()
## check your working directory for this JPEG
## use code getwd() to see this director if you don't know

As you can see from the plot,

  • Confidence band grows wider as you try to make prediction further away from you observed data;
  • Prediction interval is wider than confidence interval.

If you want to know more about how predict.lm() computes confidence / prediction intervals internally, read How does predict.lm() compute confidence interval and prediction interval?, and my answer there.

Thanks to Alex's demonstration of simple use of visreg package; but I still prefer to using R base.

Upvotes: 5

Alex
Alex

Reputation: 4995

You can simply use visreg::visreg 

library(visreg)
visreg(model)

enter image description here

If you are interested in the values:

> head(visreg(model)$fit)
        date   value visregFit visregLwr visregUpr
1 2012-12-31 13434.5  10753.10  9909.073  11597.13
2 2013-01-10 13434.5  10807.81  9974.593  11641.02
3 2013-01-21 13434.5  10862.52 10040.033  11685.00
4 2013-02-01 13434.5  10917.22 10105.389  11729.06
5 2013-02-12 13434.5  10971.93 10170.658  11773.21
6 2013-02-23 13434.5  11026.64 10235.837  11817.44

Upvotes: 2

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