Rolling regression and prediction with lm() and predict()

Question

I need to apply lm() to an enlarging subset of my dataframe dat, while making prediction for the next observation. For example, I am doing:

Answer 1

I just made up some random data to use for this example. I'm calling the object data because that was what it was called in the question at the time that I wrote this solution (call it anything you like).

(Efficient) Solution

data <- data.frame(v1=rnorm(100),v2=rnorm(100),clicks=rnorm(100))

data1 = data[1:(nrow(data)-1), ]
data2 = data[nrow(data), ]

for(i in 3:nrow(data)){
  nam  <- paste("predict", i, sep = "")
  nam1 <- paste("fit", i, sep = "")
  nam2 <- paste("summary_fit", i, sep = "")

  fit = lm(clicks ~ v1 + v2, data=data[1:i,])
  tmp  <- predict(fit, newdata=data2, se.fit=TRUE)
  tmp1 <- fit
  tmp2 <- summary(fit)
  assign(nam, tmp)
  assign(nam1, tmp1)
  assign(nam2, tmp2)
}

All of the results you want will be stored in the data objects this creates.

For example:

> summary_fit10$r.squared
[1] 0.3087432

You mentioned in the comments that you'd like a table of results. You can programmatically create tables of results from the 3 types of output files like this:

rm(data,data1,data2,i,nam,nam1,nam2,fit,tmp,tmp1,tmp2)
frames <- ls()

frames.fit     <- frames[1:98] #change index or use pattern matching as needed
frames.predict <- frames[99:196]
frames.sum     <- frames[197:294]

fit.table <- data.frame(intercept=NA,v1=NA,v2=NA,sourcedf=NA)
for(i in 1:length(frames.fit)){
  tmp <- get(frames.fit[i])
  fit.table              <- rbind(fit.table,c(tmp$coefficients[[1]],tmp$coefficients[[2]],tmp$coefficients[[3]],frames.fit[i]))
}

fit.table

> fit.table
             intercept                   v1                   v2 sourcedf
2  -0.0647017971121678     1.34929652763687   -0.300502017324518    fit10
3  -0.0401617893034109   -0.034750571912636  -0.0843076273486442   fit100
4   0.0132968863522573     1.31283604433593   -0.388846211083564    fit11
5   0.0315113918953643     1.31099122173898   -0.371130010135382    fit12
6    0.149582794027583    0.958692838785998   -0.299479715938493    fit13
7  0.00759688947362175    0.703525856001948   -0.297223988673322    fit14
8    0.219756240025917    0.631961979610744   -0.347851129205841    fit15
9     0.13389223748979    0.560583832333355   -0.276076134872669    fit16
10   0.147258022154645    0.581865844000838   -0.278212722024832    fit17
11  0.0592160359650468    0.469842498721747   -0.163187274356457    fit18
12   0.120640756525163    0.430051839741539   -0.201725012088506    fit19
13   0.101443924785995     0.34966728554219   -0.231560038360121    fit20
14  0.0416637001406594    0.472156988919337   -0.247684504074867    fit21
15 -0.0158319749710781    0.451944113682333   -0.171367482879835    fit22
16 -0.0337969739950376    0.423851304105399   -0.157905431162024    fit23
17  -0.109460218252207     0.32206642419212   -0.055331391802687    fit24
18  -0.100560410735971    0.335862465403716  -0.0609509815266072    fit25
19  -0.138175283219818    0.390418411384468  -0.0873106257144312    fit26
20  -0.106984355317733    0.391270279253722  -0.0560299858019556    fit27
21 -0.0740684978271464    0.385267011513678  -0.0548056844433894    fit28