Tag: C++

Example 2014.11: Contrasts the basic way for R

As we discuss in section 6.1.4 of the second edition, R and SAS handle categorical variables and their parameterization in models quite differently. SAS treats them on a procedure-by-procedure basis, which leads to some odd differences in capabilities and default parameterizations. For example, in the logistic procedure, the default is effect cell coding, while in the genmod procedure– which also fits logistic regression– the default is reference cell coding. Meanwhile, many procedures can only accommodate reference cell coding.

In R, in contrast, categorical variables can be designated as “factors” and parameterization stored an attribute of the factor.

In section 6.1.4, we demonstrate how the parameterization of a factor can be easily changed on the fly, in R, in lm(),glm(), and aov, using the contrasts= option in those functions. Here we show how to set the attribute more generally, for use in functions that don’t accept the option. This post was inspired by a question from Julia Kuder, of Brigham and Women’s Hospital.

SAS
We begin by simulating censored survival data as in Example 7.30. We’ll also export the data to use in R.


data simcox;
beta1 = 2;
lambdat = 0.002; *baseline hazard;
lambdac = 0.004; *censoring hazard;
do i = 1 to 10000;
x1 = rantbl(0, .25, .25,.25);
linpred = exp(-beta1*(x1 eq 4));
t = rand("WEIBULL", 1, lambdaT * linpred);
* time of event;
c = rand("WEIBULL", 1, lambdaC);
* time of censoring;
time = min(t, c); * which came first?;
censored = (c lt t);
output;
end;
run;

proc export data=simcox replace
outfile="c:/temp/simcox.csv"
dbms=csv;
run;

Now we’ll fit the data in SAS, using effect coding.


proc phreg data=simcox;
class x1 (param=effect);
model time*censored(0)= x1 ;
run;

We reproduce the rather unexciting results here for comparison with R.


Parameter Standard
Parameter DF Estimate Error

x1 1 1 -0.02698 0.03471
x1 2 1 -0.01211 0.03437
x1 3 1 -0.05940 0.03458

R
In R we read the data in, then use the C() function to assign the contr.sum contrast to a version of the x1 variable that we save as a factor. Once that is done, we can fit the proportional hazards regression with the desired contrast.


simcoxsc2 = transform(simcox, x1.eff = C(as.factor(x1), contr.sum(4)))
effmodel summary(effmodel)

We excerpt the relevant output to demonstrate equivalence with SAS.


coef exp(coef) se(coef)
x1.eff1 -0.02698 0.97339 0.03471
x1.eff2 -0.01211 0.98797 0.03437
x1.eff3 -0.05940 0.94233 0.03458

An unrelated note about aggregators:We love aggregators! Aggregators collect blogs that have similar coverage for the convenience of readers, and for blog authors they offer a way to reach new audiences. SAS and R is aggregated by R-bloggers, PROC-X, and statsblogs with our permission, and by at least 2 other aggregating services which have never contacted us. If you read this on an aggregator that does not credit the blogs it incorporates, please come visit us at SAS and R. We answer comments there and offer direct subscriptions if you like our content. In addition, no one is allowed to profit by this work under our license; if you see advertisements on this page, the aggregator is violating the terms by which we publish our work.->

C# Dictionary of US State Abbreivation to Names using SAS

I need C# code to convert United States state postal abbreviations (like AL) to full state names (like Alabama), but I’m afraid of making a typo retyping the abbreviation list from the USPS—and that just seems tedious. However, it’s e…

Too Big to Be Accurate(1): Which is the Most Powerful Calculator in the World?

Calculate the factorial of 171 (171!)? Just TRY! It is equal to 171*170*169*….2*1.
1. Google calculator
As Google fanatics, I first try to search the answer via Google:

Whoops, nothing interested returned! Type “170!” and get the output:
Why kinda things happened in this calculator? 171! is just equal to 171*170!.
2. Excel
Switch to Excel spreadsheet. Function fact(*) […]

SAS Data Step’s Built-in Loop: An illustrated Example

Some newbie SAS programmers take SAS as their first programming language even learned. Sometimes they are confused by the concept of “data step’s built-in loop” even after reading the well-written The Little SAS Book: A Primer:
DATA steps also have an underlying structure, an implicit, built-in loop. You don’t tell SAS to execute this loop: SAS […]