Tags: Statistics R


In psychology, we typically learn how to calculate OLS regression by calculating each coefficient separately. However, I recently learned how to calculate this using matrix algebra. Here is a brief tutorial on how to perform this using R.


R Packages

packages <- c("tidyverse", "broom")
xfun::pkg_attach(packages, message = F)


Dataset

dataset <- carData::Salaries %>%
  select(salary, yrs.since.phd) %>%
  mutate(yrs.since.phd = scale(yrs.since.phd, center = T, scale = F))
summary(dataset)
##      salary        yrs.since.phd.V1  
##  Min.   : 57800   Min.   :-21.31486  
##  1st Qu.: 91000   1st Qu.:-10.31486  
##  Median :107300   Median : -1.31486  
##  Mean   :113706   Mean   :  0.00000  
##  3rd Qu.:134185   3rd Qu.:  9.68514  
##  Max.   :231545   Max.   : 33.68514

The Salaries dataset is from the carData package, which shows the salary of professors in the US during the academic year of 2008 and 2009. Let’s say we are interested in determining if professors who have had their Ph.D. degree for longer are more likely to also have higher salaries.


Solve Using Matrix Algebra

Design Matrix

The design matrix is just a dataset of the all the predictors, which includes the intercept set at 1 and yrs.since.phd.

x <- tibble(
  intercept = 1,
  yrs.since.phd = as.numeric(dataset$yrs.since.phd)
) %>%
  as.matrix()
head(x)
##      intercept yrs.since.phd
## [1,]         1     -3.314861
## [2,]         1     -2.314861
## [3,]         1    -18.314861
## [4,]         1     22.685139
## [5,]         1     17.685139
## [6,]         1    -16.314861


Dependent Variable

y <- dataset$salary %>% as.matrix()
head(y)
##        [,1]
## [1,] 139750
## [2,] 173200
## [3,]  79750
## [4,] 115000
## [5,] 141500
## [6,]  97000


\(X'X\)

First, we need to solve for \(X'X\), which is the transposed design matrix (\(X'\)) multiplied by the design matrix (\(X\)).

Let’s take a look at what \(X'\) looks like.

x_transposed <- t(x)
x_transposed[, 1:6]
##                    [,1]      [,2]      [,3]     [,4]     [,5]      [,6]
## intercept      1.000000  1.000000   1.00000  1.00000  1.00000   1.00000
## yrs.since.phd -3.314861 -2.314861 -18.31486 22.68514 17.68514 -16.31486


After multiplication, the matrix provides the total number of participants (\(n\) = 397; really, the sum of the intercept), sum of yrs.since.phd (\(\Sigma(yrs.since.phd)\) = 0), and sum of squared yrs.since.phd (\(\Sigma (yrs.since.phd^2)\) = 65765.64). Respectively, \(\Sigma (years.since.phd)\) and \(\Sigma (yrs.since.phd^2)\) are sum of error (\(\Sigma(yrs.since.phd-M_{yrs.since.phd})\)) and sum of squared error (\(\Sigma(yrs.since.phd-M_{yrs.since.phd})^2\)) because we first centered the yrs.since.phd variable.

x_prime_x <- (x_transposed %*% x)
x_prime_x %>% round(., 2)
##               intercept yrs.since.phd
## intercept           397          0.00
## yrs.since.phd         0      65765.64


Let’s verify this.

colSums(x) %>% round(., 2)
##     intercept yrs.since.phd 
##           397             0
colSums(x^2) %>% round(., 2)
##     intercept yrs.since.phd 
##        397.00      65765.64


\((X'X)^{-1}\)

\((X'X)^{-1}\) is the inverse matrix of \(X'X\).

x_prime_x_inverse <- solve(x_prime_x)
x_prime_x_inverse
##                  intercept yrs.since.phd
## intercept     2.518892e-03  9.280150e-20
## yrs.since.phd 9.280150e-20  1.520551e-05


\(X'Y\)

\(X'Y\) contains the sum of Y (\(\Sigma Y\) = 45141464) and sum of \(XY\) (\(\Sigma XY\) = 64801658).

x_prime_y <- x_transposed %*% y
x_prime_y
##                   [,1]
## intercept     45141464
## yrs.since.phd 64801658


Let’s verify this.

sum(y)
## [1] 45141464
sum(x[, 2] * y)
## [1] 64801658


Coefficients

To obtain the coefficients, we can multiply these last two matrices (\(b = (X'X)^{-1}X'Y\)).

coef <- x_prime_x_inverse %*% x_prime_y
coef
##                      [,1]
## intercept     113706.4584
## yrs.since.phd    985.3421


Standard Error

To calculate the standard error, we multiply the inverse matrix of \(X'X\) by the mean squared error (MSE) of the model and take the square root of its diagonal matrix (\(\sqrt{diag((X'X)^{-1} * MSE)}\)).


First, we need to calculate the \(MSE\) of the model. Calculating \(MSE\) of the model is still the same, \(MSE = \frac{\Sigma(Y-\hat{Y})^{2}}{n-p} = \frac{\Sigma(e^2)}{df}\) where \(Y\) is the DV, \(\hat{Y}\) is the predicted DV, \(n\) is the total number of participants (or data points), and \(p\) is the total number of variables in the design matrix (or predictors, which includes the intercept).


To obtain the predicted values (\(\hat{Y}\)), we can also use matrix algebra by multiplying the design matrix with the coefficients (\(\hat{Y} = Xb\)).

y_predicted <- x %*% coef
head(y_predicted)
##           [,1]
## [1,] 110440.19
## [2,] 111425.53
## [3,]  95660.05
## [4,] 136059.08
## [5,] 131132.37
## [6,]  97630.74


Now that we have \(\hat{Y}\), we can then calculate the \(MSE\).

e <- y - y_predicted
se <- sum(e^2)
n <- nrow(x)
p <- ncol(x)
df <- n - p
mse <- se / df
mse
## [1] 758098328


Then, we multiply \((X'X)^{-1}\) by MSE.

mse_coef <- x_prime_x_inverse * mse
mse_coef %>% round(., 2)
##               intercept yrs.since.phd
## intercept       1909568          0.00
## yrs.since.phd         0      11527.27


Then, we take the square root of the diagonal matrix to obtain the standard error of the coefficients.

rmse_coef <- sqrt(diag(mse_coef))
rmse_coef %>% round(., 2)
##     intercept yrs.since.phd 
##       1381.87        107.37


t-Statistic

The t-statistic is just the coefficient divided by the standard error of the coefficient.

t_statistic <- as.numeric(coef) / as.numeric(rmse_coef)
t_statistic
## [1] 82.284421  9.177488


p-Value

We want the probability of obtaining that score or more extreme and not the other way around. Thus, we need to set lower to FALSE. Also, we need to multiply it by 2 to obtain a two-tailed test.

p_value <- 2 * pt(t_statistic, df, lower = FALSE)
p_value
## [1] 1.070665e-250  2.495042e-18


Summary

tibble(
  term = colnames(x),
  estimate = as.numeric(coef),
  std.error = as.numeric(rmse_coef),
  statistic = as.numeric(t_statistic),
  p.value = as.numeric(p_value)
)
## # A tibble: 2 x 5
##   term          estimate std.error statistic   p.value
##   <chr>            <dbl>     <dbl>     <dbl>     <dbl>
## 1 intercept      113706.     1382.     82.3  1.07e-250
## 2 yrs.since.phd     985.      107.      9.18 2.50e- 18


Solve Using lm Function

lm(salary ~ yrs.since.phd, dataset) %>% summary() %>% tidy()
## # A tibble: 2 x 5
##   term          estimate std.error statistic   p.value
##   <chr>            <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)    113706.     1382.     82.3  1.07e-250
## 2 yrs.since.phd     985.      107.      9.18 2.50e- 18




---
title: "Solving Ordinary Least Squares (OLS) Regression Using Matrix Algebra"
date: "2019-01-30"
output:
  html_document:
    highlight: textmate
    theme: lumen
    code_download: true
    toc: yes
    toc_float:
      collapsed: yes
      smooth_scroll: yes
---

<br>*Tags:* <span class="badge badge-pill badge-light">Statistics</span>
<span class="badge badge-pill badge-primary">R</span>

<br>

In psychology, we typically learn how to calculate OLS regression by calculating each coefficient separately. However, I recently learned how to calculate this using matrix algebra. Here is a brief tutorial on how to perform this using R.

<br>

## R Packages
```{r}
packages <- c("tidyverse", "broom")
xfun::pkg_attach(packages, message = F)
```

<br>

## Dataset
```{r}
dataset <- carData::Salaries %>%
  select(salary, yrs.since.phd) %>%
  mutate(yrs.since.phd = scale(yrs.since.phd, center = T, scale = F))
```

```{r}
summary(dataset)
```

The `Salaries` dataset is from the `carData` package, which shows the salary of professors in the US during the academic year of 2008 and 2009. Let's say we are interested in determining if professors who have had their Ph.D. degree for longer are more likely to also have higher salaries.

<br>

## Solve Using Matrix Algebra

### Design Matrix

The design matrix is just a dataset of the all the predictors, which includes the `intercept` set at 1 and `yrs.since.phd`.

```{r}
x <- tibble(
  intercept = 1,
  yrs.since.phd = as.numeric(dataset$yrs.since.phd)
) %>%
  as.matrix()
head(x)
```

<br>

### Dependent Variable
```{r}
y <- dataset$salary %>% as.matrix()
head(y)
```

<br>

### $X'X$

First, we need to solve for $X'X$, which is the transposed design matrix ($X'$) multiplied by the design matrix ($X$). 

Let's take a look at what $X'$ looks like.

```{r}
x_transposed <- t(x)
x_transposed[, 1:6]
```

<br>

After multiplication, the matrix provides the total number of participants ($n$ = 397; really, the sum of the intercept), sum of `yrs.since.phd` ($\Sigma(yrs.since.phd)$ = 0), and sum of squared `yrs.since.phd` ($\Sigma (yrs.since.phd^2)$ = 65765.64). Respectively, $\Sigma (years.since.phd)$ and $\Sigma (yrs.since.phd^2)$ are sum of error ($\Sigma(yrs.since.phd-M_{yrs.since.phd})$) and sum of squared error ($\Sigma(yrs.since.phd-M_{yrs.since.phd})^2$) because we first centered the `yrs.since.phd` variable.

```{r}
x_prime_x <- (x_transposed %*% x)
x_prime_x %>% round(., 2)
```

<br>

Let's verify this.
```{r}
colSums(x) %>% round(., 2)
colSums(x^2) %>% round(., 2)
```

<br>

### $(X'X)^{-1}$

$(X'X)^{-1}$ is the inverse matrix of $X'X$.
```{r}
x_prime_x_inverse <- solve(x_prime_x)
x_prime_x_inverse
```

<br>

### $X'Y$

$X'Y$ contains the sum of Y ($\Sigma Y$ = 45141464) and sum of $XY$ ($\Sigma XY$ = 64801658).
```{r}
x_prime_y <- x_transposed %*% y
x_prime_y
```

<br>

Let's verify this.
```{r}
sum(y)

sum(x[, 2] * y)
```

<br>

### Coefficients

To obtain the coefficients, we can multiply these last two matrices ($b = (X'X)^{-1}X'Y$).
```{r}
coef <- x_prime_x_inverse %*% x_prime_y
coef
```

<br>

### Standard Error

To calculate the standard error, we multiply the inverse matrix of $X'X$ by the mean squared error (MSE) of the model and take the square root of its diagonal matrix ($\sqrt{diag((X'X)^{-1} * MSE)}$).

<br>

First, we need to calculate the $MSE$ of the model. Calculating $MSE$ of the model is still the same, $MSE = \frac{\Sigma(Y-\hat{Y})^{2}}{n-p} = \frac{\Sigma(e^2)}{df}$ where $Y$ is the DV, $\hat{Y}$ is the predicted DV, $n$ is the total number of participants (or data points), and $p$ is the total number of variables in the design matrix (or predictors, which includes the intercept). 

<br>

To obtain the predicted values ($\hat{Y}$), we can also use matrix algebra by multiplying the design matrix with the coefficients ($\hat{Y} = Xb$).
```{r}
y_predicted <- x %*% coef
head(y_predicted)
```

<br>

Now that we have $\hat{Y}$, we can then calculate the $MSE$.
```{r}
e <- y - y_predicted
se <- sum(e^2)
n <- nrow(x)
p <- ncol(x)
df <- n - p
mse <- se / df
mse
```

<br>

Then, we multiply $(X'X)^{-1}$ by MSE.

```{r}
mse_coef <- x_prime_x_inverse * mse
mse_coef %>% round(., 2)
```

<br>

Then, we take the square root of the diagonal matrix to obtain the standard error of the coefficients.

```{r}
rmse_coef <- sqrt(diag(mse_coef))
rmse_coef %>% round(., 2)
```

<br>

### *t*-Statistic

The *t*-statistic is just the coefficient divided by the standard error of the coefficient.

```{r}
t_statistic <- as.numeric(coef) / as.numeric(rmse_coef)
t_statistic
```

<br>

### *p*-Value

We want the probability of obtaining that score or more extreme and not the other way around. Thus, we need to set lower to FALSE. Also, we need to multiply it by 2 to obtain a two-tailed test.

```{r}
p_value <- 2 * pt(t_statistic, df, lower = FALSE)
p_value
```

<br>

### Summary
```{r}
tibble(
  term = colnames(x),
  estimate = as.numeric(coef),
  std.error = as.numeric(rmse_coef),
  statistic = as.numeric(t_statistic),
  p.value = as.numeric(p_value)
)
```

<br>

## Solve Using `lm` Function
```{r}
lm(salary ~ yrs.since.phd, dataset) %>% summary() %>% tidy()
```

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