# Transforming variables for multiple regression in R

I am trying to perform a multiple regression in R. However, my dependent variable has the following plot:

Here is a scatterplot matrix with all my variables (WAR is the dependent variable):

I know that I need to perform a transformation on this variable (and possibly the independent variables?) but I am not sure of the exact transformation required. Can someone point me in the right direction? I am happy to provide any additional information about the relationship between the independent and dependent variables.

The diagnostic graphics from my regression look as follows:

EDIT

After transforming the dependent and independent variables using Yeo-Johnson transformations, the diagnostic plots look like this:

If I use a GLM with a log-link, the diagnostic graphics are:

• Hi @zglaa1 and welcome. Why do you think that you have to transform the variables? The first step would be to fit the regression with the original varibales and then look at the fit (residuals etc.). The residuals should approximately normally distributed, not the variables. Maybe you'll find this post interesting. – COOLSerdash Jun 8 '13 at 13:26
• Thank for you for both the link and the suggestion. I have run my regression and I know the variables need to be transformed based on the following plot: i.imgur.com/rbmu14M.jpg I can see unbiasedness and lack of constant variability in the residuals. Also, they are not normal. – zgall1 Jun 8 '13 at 13:40
• @COOLSerdash I took a look at the link. I have a basic background in statistics so I understand the discussion. However, my problem is that I have limited experience with actually applying the techniques I have learned so I struggle to figure out what exactly I need to do with my data (either in Excel or R) to actually perform the necessary transformations. – zgall1 Jun 8 '13 at 13:45
• Thanks for the graphic. You are absolutely right by saying that this fit is suboptimal. Could you please produce a scatterplot matrix with the DV and IVs in the regression? This can be done in R with the command pairs(my.data, lower.panel = panel.smooth) where my.data would be your dataset. – COOLSerdash Jun 8 '13 at 13:49
• A general approach to transformation are Box-Cox transformations. What you could do is the following: 1. Fit your regression model with lm using the untransformed variables. 2. Use the function boxcox(my.lm.model) from the MASS package to estimate $\lambda$. The command also produces a graphic that you could upload for our convenience. – COOLSerdash Jun 8 '13 at 14:26

John Fox's book An R companion to applied regression is an excellent ressource on applied regression modelling with R. The package car which I use throughout in this answer is the accompanying package. The book also has as website with additional chapters.

## Transforming the response (aka dependent variable, outcome)

Box-Cox transformations offer a possible way for choosing a transformation of the response. After fitting your regression model containing untransformed variables with the R function lm, you can use the function boxCox from the car package to estimate $\lambda$ (i.e. the power parameter) by maximum likelihood. Because your dependent variable isn't strictly positive, Box-Cox transformations will not work and you have to specify the option family="yjPower" to use the Yeo-Johnson transformations (see the original paper here and this related post):

boxCox(my.regression.model, family="yjPower", plotit = TRUE)


This produces a plot like the following one:

The best estimate of $\lambda$ is the value that maximizes the profile likelhod which in this example is about 0.2. Usually, the estimate of $\lambda$ is rounded to a familiar value that is still within the 95%-confidence interval, such as -1, -1/2, 0, 1/3, 1/2, 1 or 2.

To transform your dependent variable now, use the function yjPower from the car package:

depvar.transformed <- yjPower(my.dependent.variable, lambda)


In the function, the lambda should be the rounded $\lambda$ you have found before using boxCox. Then fit the regression again with the transformed dependent variable.

Important: Rather than just log-transform the dependent variable, you should consider to fit a GLM with a log-link. Here are some references that provide further information: first, second, third. To do this in R, use glm:

glm.mod <- glm(y~x1+x2, family=gaussian(link="log"))


where y is your dependent variable and x1, x2 etc. are your independent variables.

## Transformations of predictors

Transformations of strictly positive predictors can be estimated by maximum likelihood after the transformation of the dependent variable. To do so, use the function boxTidwell from the car package (for the original paper see here). Use it like that: boxTidwell(y~x1+x2, other.x=~x3+x4). The important thing here is that option other.x indicates the terms of the regression that are not to be transformed. This would be all your categorical variables. The function produces an output of the following form:

boxTidwell(prestige ~ income + education, other.x=~ type + poly(women, 2), data=Prestige)

Score Statistic   p-value MLE of lambda
income          -4.482406 0.0000074    -0.3476283
education        0.216991 0.8282154     1.2538274


In that case, the score test suggests that the variable income should be transformed. The maximum likelihood estimates of $\lambda$ for income is -0.348. This could be rounded to -0.5 which is analogous to the transformation $\text{income}_{new}=1/\sqrt{\text{income}_{old}}$.

Another very interesting post on the site about the transformation of the independent variables is this one.

While log-transformed dependent and/or independent variables can be interpreted relatively easy, the interpretation of other, more complicated transformations is less intuitive (for me at least). How would you, for example, interpret the regression coefficients after the dependent variables has been transformed by $1/\sqrt{y}$? There are quite a few posts on this site that deal exactly with that question: first, second, third, fourth. If you use the $\lambda$ from Box-Cox directly, without rounding (e.g. $\lambda$=-0.382), it is even more difficult to interpret the regression coefficients.

## Modelling nonlinear relationships

Two quite flexible methods to fit nonlinear relationships are fractional polynomials and splines. These three papers offer a very good introduction to both methods: First, second and third. There is also a whole book about fractional polynomials and R. The R package mfp implements multivariable fractional polynomials. This presentation might be informative regarding fractional polynomials. To fit splines, you can use the function gam (generalized additive models, see here for an excellent introduction with R) from the package mgcv or the functions ns (natural cubic splines) and bs (cubic B-splines) from the package splines (see here for an example of the usage of these functions). Using gam you can specify which predictors you want to fit using splines using the s() function:

my.gam <- gam(y~s(x1) + x2, family=gaussian())


here, x1 would be fitted using a spline and x2 linearly as in a normal linear regression. Inside gam you can specify the distribution family and the link function as in glm. So to fit a model with a log-link function, you can specify the option family=gaussian(link="log") in gam as in glm.

Have a look at this post from the site.

• Good explanation. I don't know that explicit Box-Cox is really the most common method of choosing a transformation. If you count people who just choose logs any way, my own wild guess is that it's a minority method. That picky point doesn't affect anything else, naturally. – Nick Cox Jun 8 '13 at 15:39
• @NickCox Thanks (+1 for your answer, btw). The statement that Box-Cox is the most common method comes from John Fox's book. I took it at face value as I don't have enough experience to judge the statement. I'll remove the statement. – COOLSerdash Jun 8 '13 at 15:44
• Thank you so much for the detailed explanation. I will try and apply it to my data now. – zgall1 Jun 8 '13 at 15:46
• @COOLSerdash Using your detailed walkthrough, I applied the Box Cox transformation to my dependent and then independent variables and have the following plot of my diagnostic variables - i.imgur.com/eO01djl.jpg Clearly, there is an improvement but there still seems to be issues with constant variability and unbiasedness and there is definitely an issue with normality. Where can I go from here? – zgall1 Jun 8 '13 at 16:52
• @zgall1 Thanks for your feedback, I appreciate it. Hm, yes, the transformations didn't seem to have helped much :). At this point, I would probabily try to use splines for the predictors using generalized additive models (GAMs) with the mgcv package and gam. If that doesn't help, I'm at my wit's end I'm afraid. There are people here that are far more experienced than me and maybe they can give you further advice. I am also not knowledgeable with baseball. Maybe there is a more logical model that makes sense with these data. – COOLSerdash Jun 8 '13 at 17:02

You should tell us more about the nature of your response (outcome, dependent) variable. From your first plot it is strongly positively skewed with many values near zero and some negative. From that it is possible, but not inevitable, that transformation would help you, but the most important question is whether transformation would make your data closer to a linear relationship.

Note that negative values for the response rule out straight logarithmic transformation, but not log(response + constant), and not a generalised linear model with logarithmic link.

There are many answers on this site discussing log(response + constant), which divides statistical people: some people dislike it as being ad hoc and difficult to work with, while others regard it as a legitimate device.

A GLM with log link is still possible.

Alternatively, it may be that your model reflects some kind of mixed process, in which case a customised model reflecting the data generation process more closely would be a good idea.

(LATER)

The OP has a dependent variable WAR with values ranging roughly from about 100 to -2. To get over problems with taking logarithms of zero or negative values, OP proposes a fudge of zeros and negatives to 0.000001. Now on a logarithmic scale (base 10) those values range from about 2 (100 or so) through to -6 (0.000001). The minority of fudged points on a logarithmic scale are now a minority of massive outliers. Plot log_10(fudged WAR) against anything else to see this.

• As you might be able to tell from the scatterplot posted above, I am using a baseball statistics data set. The independent variable, WAR, is essentially a cumulative measure of the value contributed by a player over their career at the major league level. The independent variables, AdjSLG, SOPct and BBPct are minor league statistics that are commonly thought to predict success at the major league level. The Age variable is the age at which the player produced the minor league statistics. The dummy variables are used to indicate the minor league level at which the statistics were produced. – zgall1 Jun 8 '13 at 14:08
• With regards to the negative independent variable (WAR) issue, for reasons that are a bit complex, it is reasonable to re-code those as zeros if that makes the transformation process easier. Within the framework of this dataset, this is a justifiable procedure. If you would like me to go into more detail (warning - baseball jargon required), I am happy to do so. – zgall1 Jun 8 '13 at 14:09
• It seems that WAR is your dependent variable. You provide evidence for my assertion, disputed elsewhere on this site, that the two terms are often confused. My advice is not to recode negative values to zeros (maltreats the data) but to use a GLM with log link. Please assume zero interest in, or knowledge of, baseball minutiae on my side. – Nick Cox Jun 8 '13 at 14:39
• You are correct that WAR is my dependent variable. I will look into a GLM with log link. Thanks for the advice. – zgall1 Jun 8 '13 at 14:41
• Might be helpful to know how career WAR is calculated then (aka understand the data generating process). – Affine Jun 8 '13 at 19:13