Dr. Mark Gardener

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Index of MonogRaphs

# R: MonogRaphs

### A series of essays on random topics using R: The Statistical Programming Language

R is a powerful and flexible beast. Getting started using R is not too difficult and you can learn to start using R in an afternoon. However, mastering R takes rather longer! These monographs are my way of exploring various topics in a completely unstructured manner.

# Beta coefficients from linear models

In linear regression your aim is to describe the data in terms of a (relatively) simple equation. The simplest form of regression is between two variables:

y = mx + c

In the equation y represents the response variable and x is a single predictor variable. The slope, m, and the intercept, c, are known as coefficients. If you know the values of these coefficients then you can plug them into the formula for values of x, the predictor, and produce a value for the response.

In multiple regression you "extend" the formula to obtain coefficients for each of the predictors.

y = m1.x1 + m2.x2 + m3.x3 + ... + c

If you standardize the coefficients (using standard deviation of response and predictor) you can compare coefficients against one another, as they effectively assume the same units/scale.

The functions for computing beta coefficients are not built-in to R. In these notes you'll see some custom R commands that allow you to get the beta coefficients easily. You can download the Beta coeff calc.R file and use it how you like.

## Contents

Beta coefficients are regression coefficients that are standardized against one another so that direct effects can be compared.

You can calculate beta coefficients from correlation coeffients.

You can calculate beta coefficients from regression coefficients.

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## What are beta coefficients?

Beta coefficients are regression coefficients (analogous to the slope in a simple regression/correlation) that are standardized against one another. This standardization means that they are "on the same scale", or have the same units, which allows you to compare the magnitude of their effects directly.

### Beta coefficients from correlation

It is possible to calculate beta coefficients more or less directly, if you have the correlation coefficient, r, between the various components:

The subscripts can be confusing but essentially you can use a similar formula for the different combinations of variables.

### Beta coefficients from regression coefficients

In most cases you'll have calculated the regression coefficients (slope, intercept) and you can use these, along with standard deviation, to calculate the beta coefficients.

This formula is a lot easier to understand: b' is the beta coefficient, b is the standard regression coefficient. The x and y refer to the predictor and response variables. You therefore take the standard deviation of the predictor variable, divide by the standard deviation of the response and multiply by the regression coefficient for the predictor under consideration.

There are no built-in functions that will calculate the beta coefficients for you, so I wrote one myself. The command(s) are easy to run/use and I've annotated the script/code fairly heavily as it should be useful/helpful for others to see what's happening.

Use beta.coef() to calculate beta coefficients from a regression model result.

beta.coef() has print and summary methods.

Beta coeff calc.R

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### The beta.coef() command

I wrote the beta.coef() command to calculate beta coefficients from lm() result objects. Here's a brief overview:

 beta.coef(model, digits = 7) print.beta.coef(object, digits = 7) summary.beta.coef(object, digits = 7) model A regression model, the result from lm(). object The result of a beta.coef() command. digits = 7 The number of digits to display, defaults to 7.

The beta.coef() command produces a result with a custom class beta.coef. The print() and summary() commands will use this class to display the coefficients or produce a more comprehensive summary (compares the regular regression coefficients and the beta).

Get beta coefficients for regression models using beta.coef()

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#### Using beta.coef()

You will need the result of a linear regression, usually this will be one with the class "lm".

```> mf.lm = lm(Length ~ BOD + Algae, data = mf)
> summary(mf.lm)```
```Call:
lm(formula = Length ~ BOD + Algae, data = mf)```
```Residuals:
Min      1Q  Median      3Q     Max
-3.1246 -0.9384 -0.2342  1.2049  3.2908 ```
```Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.34681    4.09862   5.452 1.78e-05 ***
BOD         -0.03779    0.01517  -2.492   0.0207 *
Algae        0.04809    0.03504   1.373   0.1837
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1```
```Residual standard error: 1.83 on 22 degrees of freedom
Multiple R-squared:  0.6766,	Adjusted R-squared:  0.6472
F-statistic: 23.01 on 2 and 22 DF,  p-value: 4.046e-06    ```

Once you have the result you can use the beta.coef() command to compute the beta coefficients:

`> mf.bc = beta.coef(mf.lm)`
`Beta Coefficients for: mf.lm `
```                 BOD     Algae
Beta.Coef -0.5514277 0.3037675```

Note that the result is shown even though the result was assigned to a named object.

Use print() to return the beta coefficients from a beta.coef() result.

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#### Print beta.coef results

You can use the print method to display the coefficients, setting the number of digits to display:

`> print(mf.bc, digits = 4)`
```Beta coefficients:
BOD  Algae
Beta.Coef -0.5514 0.3038    ```

The command takes the \$beta.coef component from the beta.coef() result object.

Use summary() to return the original and beta coefficients from a beta.coef() result.

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#### Summary beta.coef results

The summary method produces a result that compares the regular regression coefficients and the standardized ones (the beta coefficients). You can set the number of digits to display using the digits parameter:

`> summary(mf.bc, digits = 5)`
`Beta coefficients and lm() model summary.`
```Model call:
lm(formula = Length ~ BOD + Algae, data = mf)```
```          (Intercept)       BOD    Algae
Coef           22.347 -0.037788 0.048094
Beta.Coef          NA -0.551428 0.303768    ```

The command returns the model call as a reminder of the model.

Use source() to run the command code in your R console.

Or paste the Beta coeff calc.R file into your R console.

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To access the commands you could copy/paste the code from Beta coeff calc.R. Alternatively you can download the file and use:

> source("Beta coeff calc.R")

As long as the file is in the working directory. Alternatively on Windows or Mac use:

> source(file.choose())

The file.choose() part opens a browser-like window, allowing you to select the file. Once loaded the new commands will be visible if you type ls().

Use package: lm.beta to calculate beta coefficients and add them to regression model results.

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### The lm.beta package

After I had written the code to calculate the beta coefficients I discovered the lm.beta package on the CRAN repository.

Install the package:

> install.packages("lm.beta")

The package includes the command lm.beta() which calculates beta coefficients. The command differs from my code in that it adds the standardized coefficients (beta coefficients) to the regression model. The package commands also allow computation of beta coefficients for interaction terms.

Use the command:

> help(lm.beta)

to get more complete documentation once you have the package installed and running. You can also view the code directly (there is no annotation).

> lm.beta
> print.lm.beta
> summary.lm.beta

The lm.beta() command produces a result with two classes, the original "lm" and "lm.beta".

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