Statistics for Data Science & Analytics

Master advanced statistical modeling in SAS with our detailed question-and-answer guide. This Understanding Advanced SAS Procedures post explains the core statements and functionality of essential SAS procedures like PROC NLIN for nonlinear regression, PROC NLMIXED for nonlinear mixed models, PROC GLIMMIX for linear and generalized linear mixed models, and PROC PROBIT for dose-response analysis. Learn how to use PARMS, MODEL, RANDOM, and CLASS statements correctly, avoid common syntax errors, and interpret your results with practical examples from the sashelp.cars and sashelp.iris datasets. Perfect for data analysts and statisticians looking to deepen their SAS programming skills.

Understanding Advanced SAS Procedures

Explain the following SAS Statements used in the Example below (Non-Linear Mixed Model)

proc nlmixed data = CARS;
parms b1 = 220 b2 = 500 b3 = 310 s2u = 100 s2e = 60;
model X ~ normal(num/den, s2e);
random u1 ~ normal(0, s2u) subject = NUMBER;
run;

This is an excellent example of a nonlinear mixed model in SAS. The MIXED MODEL statement defines the dependent variable and its conditional distribution given the random effects. In the above statement, a normal (Gaussian) conditional distribution is specified.

This code is fitting a nonlinear mixed-effects model to data about cars (from the CARS dataset). It is trying to estimate parameters ($b_1, b_2$, and $b_3$) for a specific nonlinear relationship between a predictor and the outcome $X$, while also accounting for random variations between different groups of cars (grouped by NUMBER).

The RANDOM statement defines the single random effect to be $u1$, and specifies that it follows a normal distribution with mean $0$ and variance $s2u$. The SUBJECT= statement in the RANDOM statement is used to define a variable that will indicate when the random effect obtains new realizations.

Explain the following SAS statements (Linear Mixed Model) in the example below

proc glimmix data = sashelp.iris;
class species;
model age = weight;
random age = weight;
run;

The CLASS statement instructs the technique to treat the variable species as type variables. The version announcement in the example shown above specifies the reaction variable as a pattern proportion by means of the use of the occasions/trials approach.

This PROC GLIMMIX code contains a critical error in its RANDOM statement, which makes the model, as written, invalid and nonsensical.

In code, it is trying to fit a linear mixed model to the sashelp.iris dataset (famous Fisher’s Iris data). The intent might have been to see how age (which does not exist in the standard iris dataset) is related to weight (which also does not exist), while accounting for the grouping structure of species. The syntax of the RANDOM statement is completely incorrect.

Explain the use of each SAS statement (PROC PROBIT) given below

PROC PROBIT dataset;
CLASS <dependent variables>;
Model < dependent variables > = <independent VARIABLES>;

This statement outlines the basic structure for using PROC PROBIT in SAS, but it contains a few common misunderstandings and a critical error in the CLASS statement. However, the line-by-line explanation of the code is:

The DATA= option specifies the dataset that will be studied.

The PLOTS= choice within the PROC PROBIT statement, collectively with the ODS graphics announcement, requests all plots (as all have been specified in brackets, we will pick out a selected plot also) for the anticipated opportunity values and peak ranges.

The model statement prepares a response between a structured variable and independent variables. The variables top and weight are the stimuli or explanatory variables.

Explain the following SAS example (PROC NLIN)

proc nlin data = sashelp.cars method = gauss;
parms hosepower = 135
cylinders = 6;
model mpg_highway = (horsepower/cylinders);
run;

This code is used to fit a nonlinear regression model (PROC NLIN) to car data. The METHOD = option directs PROC NLIN to use the GAUSS iterative method. The PARMS statement declares the parameters and specifies their initial values.

The code is trying to model a car’s highway fuel efficiency (mpg_highway) as a simple nonlinear function of its power (horsepower) and engine size (cylinders). Specifically, it is testing the hypothesis that highway MPG is directly proportional to the power-per-cylinder (horsepower / cylinders). The code contains a critical error in its model specification, which will cause it to fail.

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