Categorical variables in logistic regression sas
This post was kindly contributed by The DO Loop - go there to comment and to read the full post. A dummy variable also known as indicator variable is a numeric variable that indicates the presence or absence of some level of a categorical variable. In regression and other statistical analyses, a categorical variable can be replaced by dummy variables. There are many ways to construct dummy variables in SAS. Some programmers use the DATA step, but there is an easier way. A subsequent blog post discusses other SAS procedures that provide alternative methods for representating categorical variables.
If a procedure contains a CLASS statement, then the procedure will automatically create and use dummy variables as part of the analysis. However, it can be useful to create a SAS data set that explicitly contains a design matrixwhich is a numerical matrix that use dummy variables to represent categorical variables. A design matrix also includes columns for continuous variables, the intercept term, and interaction effects.
A few reasons to generate a design matrix are:. The following DATA step create a data set with 10 observations. It has one continuous variable Cholesterol and two categorical variables. The first is the intercept column. The next two encode the Sex variable. If you specify interactions between the original variables, additional dummy variables are created. Notice that the order of the columns is the sort order of the values of their levels. When you use this design matrix in a regression analysis, the parameter estimates of main effects estimate the difference in the effects of each level compared to the last level in alphabetical order.
Notice that the parameter estimates for the last level are categorical variables in logistic regression sas to zero and the standard errors are assigned missing values.
This occurs because the dummy variable for each categorical variable is redundant. By setting the parameter estimate to zero, the last column for each set of dummy variables does not categorical variables in logistic regression sas to the model. For this reason, the GLM encoding is called a singular parameterization. In my next blog post I will present ways to parameterize levels of the categorical variables. These different parameterizations lead to nonsingular design matrices.
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Let me count the ways How to convert the datetime character string to SAS datetime value? How to Handle Negative Data Values? Create dummy variables in SAS February 22, Why generate dummy variables in SAS? A few reasons to generate a design matrix are: Students might need to create a design matrix so that they can fully understand the connections between regression models and matrix computations.
Another example is the MCMC procedure, whose documentation includes an example that creates a design matrix for a Bayesian regression model. In simulation studies of regression models, it is easy to generate responses by using matrix computations with a numerical design matrix. It is harder to use classification variables directly. Sponsors Dear readers, proc-x is looking for sponsors who would be willing to support the site categorical variables in logistic regression sas exchange for banner ads in the right sidebar of the site.
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