Category: SAS tips

Scatterplots – dealing with overplotting

By , January 24, 2011 5:20 pm

In a previous post, I dealt with some SAS code for scatterplots. Various problems can arise when using scatterplots. One of them is overplotting, where two or more data are the same point.

There are a variety of ways of dealing with this.
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Scatterplots and enhancements

By , January 21, 2011 7:19 am

When you have two numeric variables and are interested in the relationship between them, the basic statistical graph is the scatterplot.  These can be good, but there are ways to  enhance them and there are also alternatives which can be better in some circumstances.  In some circumstances, scatterplots can be problematic, and there are ways to deal with these problems.  In this post, I show SAS code to create a basic scatterplot and some enhanced versions.

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SAS tip: Why you always should use a RUN statement

By , July 18, 2010 5:23 pm

OK, there are lots of places where it’s written that using RUN statements makes code look cleaner, but that invocation of another PROC statement makes the previous PROC get submitted. So…. It sounds like that RUN statement is a sort of esthetic extra.

But it can bite you

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PROC LOGISTIC: Concordant and discordant

By , April 25, 2010 6:48 pm

Description of concordant and discordant in SAS PROC LOGISTIC
Part of the default output from PROC LOGISTIC is a table that has entries including`percent concordant’ and `percent discordant’. To me, this implies the percent that would correctly be assigned, based on the results of the logistic regression. But that is not what it is. It looks at all possible pairs of observations. A pair is concordant if the observation with the larger value of X also has the larger value of Y.  A pair is discordant if the observation with the larger value of X has the
smaller value of Y; here, X and Y are the predicted value and the actual value.
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PROC LOGISTIC: Complete and quasi-complete separation

By , April 13, 2010 11:26 am

Description of separation in PROC LOGISTIC

If you picture the data as a 2 x 2 crosstab, then quasi-complete separation occurs when one of the cells is 0.  Complete separation occurs when one cell in each row and column is 0.

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