proc rank

8月 102021

This post demonstrates how to rank data and how to place these ranks into roughly equal groups.

There are certain variables, such as annual salary, that are highly skewed. There are many who earn between $50,00 and $150,000, but some who earn millions or hundreds of millions of dollars a year. Trying to use variables like annual salary in statistical models typically violates assumptions of many popular statistical techniques. There are several solutions to the types of distribution problems we just described. One solution is to use a transformation like a logarithm of a value to "bring in the tail." Another solution is to substitute ranks for the original values. For example, the lowest salary would be assigned a rank of one, the next highest would be assigned a rank of two, and so forth. Another method is to place all of the values into a number of bins. For example, you could place all the salaries into ranges such that there would be approximately an equal number of values in each range.

You can use SAS Studio tasks to create ranks and, with a tiny bit of editing, create salary ranges.

Let's start with a data set called Salary that was created by a small program using a random number function. Shown below is a histogram and a smooth line representing 1,000 values of salary from this data set.

You see a grouping of values on the left side of the distribution and a few very high salaries in the right tail. For curious readers, here is the program that generated these data values.

The RAND function can generate quite a few distributions, such as uniform and normal. For this program, an exponential distribution was used.

Suppose you plan to use yearly salary in a binary logistic regression model. Using the actual values from the Salary data set would not work well. Let's start out by creating a new variable that represents the rank of salary. In SAS Studio, this is easily done using the Rank Data task as one of the selections under the Data tab. You can see this in the figure below.

You choose the data set and variable to rank on the DATA tab, like this.

The Salary data set was selected, and the variable Salary was chosen as the variable (column) to rank. Finally, Rank_Salary was selected for the output data set name. A histogram of the ranks is, as you would expect, uniform ranging from one to 1,000 (see figure below).

How can you place these 1,000 values into 10 bins? To do this, you click the CODE tab and then click Edit (circled in the figure below).

All you need to do is add the PROC RANK option Groups=10 to this program as shown next.

This option groups all the ranks into 10 groups. Below is a histogram of the variable Rank_Salary with the Groups= option included.

This new variable would work quite well in a logistic regression model or other types of regression.

If you found this blog post helpful, you might be interested in some of my books. As always, comments and/or suggestions are welcome.

How to Transform a Skewed Distribution to a Uniform Distribution was published on SAS Users.

8月 182014
In Example 8.40, side-by-side histograms, we showed how to generate histograms for some continuous variable, for each level of a categorical variable in a data set. An anonymous reader asked how we would do this if both the variables were continuous. Keep the questions coming!

The SAS solution we presented relied on the sgpanel procedure. There, the panelby statement names a variable for which each distinct value will generate a panel. If there are many values, for example for a continuous variable, there will be many panels generated, which is probably not the desired result. As far as we know, there is no option to automatically categorize a continuous panel variable in proc sgpanel. If this is required, a two-step approach will be needed to first make groups of one of the variables.

We do that below using proc rank. In this approach, the groups option is the number of groups required and the ranks statement names a new variable to hold the group indicator. Once the groups are made, the same code demonstrated earlier can be used. (This is an example of "it's never too late to learn"-- I used to do this via a sort and a data step with implied variables, until I realized that there had to be a way to it via a procedure. --KK)

In this setting, the panels are another approach to the data we examine in a scatterplot. As an example, we show the mental compentency score by grouping of the physical competency score in the HELP data set.
proc rank data = 'c:bookhelp.sas7bdat' groups = 6 out = catmcs;
var mcs;
ranks mcs_sextile;

title "Histograms of PCS by sextile of MCS";
proc sgpanel data = catmcs;
panelby mcs_sextile / columns = 3 rows =2;
histogram pcs;
We also demonstrate the columns and rows options to the panelby statement, which allow control over the presentation of the panel results. The graphic produced is shown above.

Our R solution in the earlier entry used the lattice package (written by Deepayan Sarkar) to plot a formula such as histogram(~a | b). A simple substitution of a continuous covariate b into that syntax will also generate a panel for each distinct value of the covariates: a factor is expected. In the package, an implementation of Trellis graphics, the term "shingles" is used to approach the notion of categorizing a continuous variable for making panels. The function equal.count() is provided to make the (possibly overlapping) categories of the variables, and uses the panel headers to suggest the ranges of continuous covariate that are included in each panel.
ds = read.csv("")
histogram(~ pcs | equal.count(mcs),
main="Histograms of PCS by shingle of MCS",
Note that the default ordering of panels in lattice is left to right, bottom to top. The index.cond option here re-orders the panels to go from left to right, top to bottom.

The default behavior of equal.count() is to allow some overlap between the categories, which is a little odd. In addition, there is a good deal of visual imprecision in the method used to identify the panels-- there's no key given, and the only indicator of the shingle value is the shading of the title bars. A more precise method would be to use the quantile() function manually, as we demonstrated in example 8.7, the Hosmer and Lemeshow goodness-of-fit test. We show here how the mutate() function in Hadley Wickham's dplyr package can be used to add a new variable to a data frame.

ds = mutate(ds, cutmcs = cut(ds$mcs, 
breaks = quantile(ds$mcs, probs=seq(0,1, 1/6)), include.lowest=TRUE))
histogram(~ pcs | cutmcs, main="Histograms of PCS by sextile of MCS",
index.cond=list(c(4,5,6,1,2,3)), data=ds)
This shows the exact values of the bin ranges in the panel titles, surely a better use of that space. Minor differences in the histograms are due to the overlapping categories included in the previous version.

Finally, we also show the approach one might use with the ggplot2 package, an implementation of Leland Wilkinson's Grammar of Graphics, coded by Hadley Wickham. The package includes the useful cut_number() function, which does something similar to the cut(..., breaks=quantile(...)) construction we showed above. In ggplot2, "facets" are analogous to the shingles used in lattice.
ds = mutate(ds, cutmcsgg = cut_number(ds$mcs, n=6))
ggplot(ds, aes(pcs)) + geom_bar() +
facet_wrap(~cutmcsgg) + ggtitle("Histograms of PCS by sextile of MCS")
Roughly, we can read the syntax to state: 1) make a plot from the ds dataset in which the primary analytic variable will be pcs; 2) make histograms; 3) make facets of the cutmcsgg variable; 4) add a title. Since the syntax is a little unusual, Hadley provides the qplot() function, a wrapper which operates more like traditional functions. An identical plot to the above can be generated with qplot() as follows:
qplot(data=ds,x=pcs, geom="bar", facets= ~cutmcsgg, 
main="Histograms of PCS by sextile of MCS")

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