# An R Package: Fit Repeated Linear Regressions

##### Posted on September 26, 2017 0 Comments

*Repeated Linear Regressions* refer to a set of linear regressions in which there are several same variables.

## Examples

Let’s start with the simplest situation, we want to fit a set of regressions which only differ in one variable. Specifically, denote the response variable as $y$, and these regressions are as follows.

where $cov_i, i=1,\ldots, m$ are the same variables among these regressions.

Similarly, the problem presented in the Background of the previous post is also *Repeated Linear Regressions*

## Ideas

Intuitively, we can finish this task by using a simple loop in R code.

```
model = vector(mode='list', length=n)
for (i in 1:n)
{
...
model[[i]] = lm(y~x)
}
```

However, it is not efficient in that situation. As we all know, in the linear regression, the main goal is to estimate the parameter $\beta$. And we have

where $X$ is the design matrix and $Y$ is the observation of response variable.

It is obvious that there are some same elements in the design matrix, and the larger $m$ is, the more elements are the same. So I want to reduce the cost of computation by separating the same part in the design matrix.

## Method

For the first example, the design matrix can be denoted as $X=(x, cov)$. If we consider intercept, it also can be seen as the same variable among these regression, so it can be included in $cov$ naturally. Then we have

Woodbury formula:

Let

and $C=I_{2\times 2}$. Then we can apply woodbury formula,

where

We can do further calculations to simplify and obtain the following result

Notice that matrix $B$ is the same for all regression, the identical terms for each regression are just $a$ and $v$, which are very easy to calculate. So theoretically, we can reduce the cost of computation significantly.

For the second example, the previous post has discussed its details, so I just skip this part.

## Package

You can install this package by

```
devtools::install_github('szcf-weiya/fRLR')
```

Let me check the results by two examples.

```
## use fRLR package
library(fRLR)
set.seed(123)
X = matrix(rnorm(50), 10, 5)
Y = rnorm(10)
COV = matrix(rnorm(40), 10, 4)
frlr1(X, Y, COV)
## use simple loop
res = matrix(nrow = 0, ncol = 2)
for (i in 1:ncol(X))
{
mat = cbind(X[,i], COV)
df = as.data.frame(mat)
model = lm(Y~., data = df)
tmp = c(i, summary(model)$coefficients[2, 4])
res = rbind(res, tmp)
}
```

We can get the following results.

As we can see in the above figure, these p-values for the identical variable in each regression are equal betwwen two methods.

Similarly, we can test the second example

```
library(fRLR)
set.seed(123)
X = matrix(rnorm(50), 10, 5)
Y = rnorm(10)
COV = matrix(rnorm(40), 10, 4)
idx1 = c(1, 2, 3, 4, 1, 1, 1, 2, 2, 3)
idx2 = c(2, 3, 4, 5, 3, 4, 5, 4, 5, 5)
frlr2(X, idx1, idx2, Y, COV)
res = matrix(nrow=0, ncol=4)
for (i in 1:length(idx1))
{
mat = cbind(X[, idx1[i]], X[,idx2[i]], COV)
df = as.data.frame(mat)
model = lm(Y~., data = df)
tmp = c(idx1[i], idx2[i], summary(model)$coefficients[2,4], summary(model)$coefficients[3,4])
res = rbind(res, tmp)
}
```

Again, we obtain the same results by different methods.

## Computation Cost

The main aim of this new method is to reduce the computation cost. Now let’s compare its speed with the simple-loop method.

We can obtain the following time cost for $499\times 500/2=124750$ linear regressions.

It is obvious that the `frlr`

method is much faster than the simple loop. Of course, there are many factors influence the speed, and the cost of the loop in R is very huge, while I realize the loop in Rcpp. So comparing two programs which both are in Rcpp might be much fairer. Actually, I have tested the similar results in the previous post in which the two programs are both in C++, only differ in their algorithm. As a result, separating the matrix in repeated linear regressions indeed can speed up the program.