JohnnyDeer - 4 months ago 49

R Question

I need to apply

`lm()`

`dat`

`fit model predict`

---------- -------

dat[1:3, ] dat[4, ]

dat[1:4, ] dat[5, ]

. .

. .

dat[-1, ] dat[nrow(dat), ]

I know what I should do for a particular subset (related to this question: predict() and newdata - How does this work?). For example to predict the last row, I do

`dat1 = dat[1:(nrow(dat)-1), ]`

dat2 = dat[nrow(dat), ]

fit = lm(log(clicks) ~ log(v1) + log(v12), data=dat1)

predict.fit = predict(fit, newdata=dat2, se.fit=TRUE)

How can I do this automatically for all subsets, and potentially extract what I want into a table?

- From , I'd need the
`fit`

;`summary(fit)$adj.r.squared`

- From I'd need
`predict.fit`

value.`predict.fit$fit`

Thanks.

Answer

**(Efficient) solution**

This is what you can do:

```
p <- 3 ## number of parameters in lm()
n <- nrow(dat) - 1
## a function to return what you desire for subset dat[1:x, ]
bundle <- function(x) {
fit <- lm(log(clicks) ~ log(v1) + log(v12), data = dat, subset = 1:x, model = FALSE)
pred <- predict(fit, newdata = dat[x+1, ], se.fit = TRUE)
c(summary(fit)$adj.r.squared, pred$fit, pred$se.fit)
}
## rolling regression / prediction
result <- t(sapply(p:n, bundle))
colnames(result) <- c("adj.r2", "prediction", "se")
```

Note I have done several things inside the `bundle`

function:

- I have used
`subset`

argument for selecting a subset to fit - I have used
`model = FALSE`

to not save model frame hence we save workspace

Overall, there is no obvious loop, but `sapply`

is used.

- Fitting starts from
`p`

, the minimum number of data required to fit a model with`p`

coefficients; - Fitting terminates at
`nrow(dat) - 1`

, as we at least need the final column for prediction.

**Test**

Example data (with 30 "observations")

```
dat <- data.frame(clicks = runif(30, 1, 100), v1 = runif(30, 1, 100),
v12 = runif(30, 1, 100))
```

Applying code above gives `results`

(27 rows in total, truncated output for 5 rows)

```
adj.r2 prediction se
[1,] NaN 3.881068 NaN
[2,] 0.106592619 3.676821 0.7517040
[3,] 0.545993989 3.892931 0.2758347
[4,] 0.622612495 3.766101 0.1508270
[5,] 0.180462206 3.996344 0.2059014
```

The first column is the adjusted-R.squared value for fitted model, while the second column is the prediction. The first value for `adj.r2`

is `NaN`

, because the first model we fit has 3 coefficients for 3 data points, hence no sensible statistics is available. The same happens to `se`

as well, as the fitted line has no 0 residuals, so prediction is done without uncertainty.