# Log Transformations: How to Handle Negative Data Values?

April 27, 2011
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 This post was kindly contributed by The DO Loop - go there to comment and to read the full post.

The log transformation is one of the most useful transformations in data analysis. It is used as a transformation to normality and as a variance stabilizing transformation.
A log transformation is often used as part of exploratory data analysis in order to visualize (and later model) data that ranges over several orders of magnitude. Common examples include data on income, revenue, populations of cities, sizes of things, weights of things, and so forth.

In many cases, the variable of interest is positive and the log transformation is immediately applicable. However, some quantities (for example, profit) might contain a few negative values. How do you handle negative values if you want to log-transform the data?

##### Solution 1: Translate, then Transform

A common technique for handling negative values is to add a constant value to the data prior to applying the log transform. The transformation is therefore log(Y+a) where a is the constant. Some people like to choose a so that min(Y+a) is a very small positive number (like 0.001). Others choose a so that min(Y+a) = 1. For the latter choice, you can show that a = b – min(Y), where b is either a small number or is 1.

In the SAS/IML language, this transformation is easily programmed in a single statement. The following example uses b=1 and calls the LOG10 function, but you can call LOG, the natural logarithm function, if you prefer.

```proc iml;
Y = {-3,1,2,.,5,10,100}; /** negative datum **/
LY = log10(Y + 1 - min(Y)); /** translate, then transform **/
```

##### Solution 2: Use Missing Values

A criticism of the previous method is that some practicing statisticians don’t like to add an arbitrary constant to the data. They argue that a better way to handle negative values is to use missing values for the logarithm of a nonpositive number.

This is the point at which some programmers decide to resort to loops and IF statements.
For example, some programmers write the following inefficient SAS/IML code:

```n = nrow(Y);
LogY = j(n,1); /** allocate result vector **/
do i = 1 to n; /** loop is inefficient **/
if Y>0 then LogY[i] = log(Y);
else LogY[i] = .;
end;
```

The preceding approach is fine for the DATA step, but the DO loop is completely unnecessary in PROC IML. It is more efficient to use the LOC function to assign `LogY`, as shown in the following statements.

```/** more efficient statements **/
LogY = j(nrow(Y),1,.); /** allocate missing **/
idx = loc(Y>0); /** find indices where Y>0 **/
if ncol(idx)>0 then
LogY[idx] = log10(Y[idx]);

print Y LY LogY;
```
 Y LY LogY -3 0 . 1 0.69897 0 2 0.7781513 0.30103 . . . 5 0.9542425 0.69897 10 1.146128 1 100 2.0170333 2

The preceding statements initially define `LogY` to be a vector of missing values. The LOC function finds the indices of `Y` for which `Y` is positive. If at least one such index is found, those positive values are transformed and overwrite the missing values. A missing value remains in `LogY` for any element for which `Y` is negative.

You can see why some practitioners prefer the second method over the first: the logarithms of the data
are unchanged by the second method, which makes it easy to
mentally convert the transformed data back to the original scale (see the transformed values for
1, 10, and 100). The translation method makes the mental conversion harder.

You can use the previous technique for other functions that have restricted domains. For example, the same technique applies to the SQRT function and to inverse trigonometric functions such as
ARSIN and ARCOS. This post was kindly contributed by The DO Loop - go there to comment and to read the full post.

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