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StatDave
SAS Super FREQ
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02-05-2010
04:03 PM
See these usage notes:
http://support.sas.com/kb/23087
http://support.sas.com/kb/36478
The answers to many questions can be found in the Samples and SAS Notes in the searchable knowledgebase, http://support.sas.com/kb. You can use the search engine there to find the answers you need.
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01-15-2010
04:04 PM
In PROC LOGISTIC you would actually make your 20-level variable the response, your binary group variable the predictor, and fit a generalized logit model:
proc logistic;
class group / param=ref;
model level = group / link=glogit;
run;
The tests of the GROUP parameter estimates are tests comparing the groups at each level.
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01-15-2010
02:37 PM
While the ODDSRATIO statement in LOGISTIC does not provide p-values, you could fit the same model in PROC GLIMMIX and use the ODDRATIO and SLICEDIFF= options to get the p-values. For details and an example, see this usage note:
http://support.sas.com/kb/24455
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01-15-2010
02:18 PM
If you used a distribution other than the normal in GENMOD, then the results will certainly differ from GLM which assumes a normal distribution. Other than that, GENMOD fits the model via maximum likelihood, while GLM uses ordinary least squares. The paramterization of CLASS variables is the same (non-full rank 0,1 dummy coding) by default, but you can select a different parameterization in GENMOD's CLASS statement if you specify appropriate options. If you use a different parameterization, you will get different results.
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01-15-2010
02:11 PM
See the "Using SELECTION= with many variables" and "Large input data set" sections of this usage note for some ideas:
http://support.sas.com/kb/22607
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01-15-2010
02:03 PM
You can use PROC GAM, rather than PROC LOESS, to fit a model to a logistic model to a binary response that includes loess- or spline-smoothed predictors. For details and examples, see the GAM documentation:
http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/gam_toc.htm
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11-24-2009
11:06 AM
If your response is binary, you can fit a logistic model using PROC LOGISTIC or PROC GENMOD. Neither does model selection based on AIC. However, both display the AIC for any single model that you fit (in GENMOD, AIC is displayed beginning in SAS 9.2). You can do model selection based on p-values using the SELECTION= option in PROC LOGISTIC. See also the SLENTRY= and SLSTAY= options, all in the MODEL statement.
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11-24-2009
11:01 AM
I assume you want to know how the starting values of the parameter estimates are computed. The starting values are displayed by using the ITPRINT option in the MODEL statement. The starting values are computed as discussed in the McCullagh and Nelder reference (section 2.5 in the 2nd edition). Basically, you can get them using PROC REG by regressing the logit of the response, y, on the predictors with weight ny(1-y). For example, using the remission data in the stepwise example in the PROC LOGISTIC documentation:
data a; set remission;
y=.1*(remiss=0)+.9*(remiss=1);
logit=log(y/(1-y));
wt=y*(1-y);
run;
proc reg;
model logit=smear cell;
weight wt;
run;
Note that for binary response data (rather than binomial, events/trials, data), n=1 and zeros are replaced by 0.1 and ones are replaced by 0.9. For binomial data, y=events/trials with the same adjustment if y=0 or 1.
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11-24-2009
10:41 AM
The full log likelihood includes the combinatorial (n-choose-r) that is omitted from the log likelihood. The value of the combinatorial depends on how the populations are defined and that is what the AGGREGATE= option does. If no aggregation is done, each observation is treated as a separate population of size 1.
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11-23-2009
03:28 PM
SELECTION=BACKWARD is usually a bad idea when there are many candidate effects in the model. The reason it is a bad idea is that the method starts with the most complex model which includes all of the candidate effects. This model is usually so complex and makes the data so sparse that the maximum likelihood solution does not exist. Better to use SELECTION=FORWARD or STEPWISE which starts with no effects (or only those forced by the INCLUDE= option) and built up a model.
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10-30-2009
04:11 PM
The ParameterEstimates table is the table of initial parameter estimates produced by maximum likelihood. These parameters serve only as starting values for the GEE estimation algorithm implemented by the REPEATED statement. Beginning in SAS 9.2, this table of initial parameter estimates is not displayed to avoid confusion with the estimates for the final GEE model in the GEE parameter estimates table. If you need to produce this table of initial estimates, add the PRINTMLE option in the REPEATED statement. See the "Details: ODS Table Names" section in the GENMOD documentation:
9.1: http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/genmod_index.htm
9.2: http://support.sas.com/documentation/cdl/en/statug/59654/HTML/default/genmod_toc.htm
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10-05-2009
10:58 AM
Peter,
If you just need an estimate of the area under the ROC curve, you can use the ROC macro:
http://support.sas.com/kb/25017
For instance, if your data set is named MyROC and it contains your binary response variable called Y and your predicted probability variable called PRED, this call to the ROC macro will provide point- and confidence interval estimates of the area under the ROC curve:
%roc(data=MyROC, response=Y, var=PRED)
Beginning in SAS 9.2, it's easy to do directly in PROC LOGISTIC:
proc logistic data=MyROC;
model y(event="1")=pred;
roc;
run;
See the descriptions of the ROC and ROCCONTRAST statements in the SAS 9.2 LOGISTIC documentation:
http://support.sas.com/documentation/cdl/en/statug/59654/HTML/default/logistic_toc.htm
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09-22-2009
03:57 PM
I think you'll find section 4.2.8 of the book "Analysis of Clinical Trials Using SAS: A Practical Guide" (Dmitrienko,A., et. al., Cary,NC:SAS Institute, 2005) helpful.
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09-14-2009
03:30 PM
The exponential distribution is obtained when the scale parameter of the gamma distribution (nu in the GENMOD documentation) is 1. So, you can fit a model to exponential data by simply adding the SCALE=1 and NOSCALE options in the MODEL statement. The NOSCALE option keeps the scale parameter fixed at the SCALE=1 setting and gives you a Lagrange multiplier test of the nu=1 restriction.
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08-26-2009
03:27 PM
See this usage note on the available logistic models in SAS:
http://support.sas.com/kb/22871
If you have repeated measurements on subjects, then you'll probably want to use the REPEATED statement in PROC GENMOD. See the discussion and example in the GENMOD documentation. PROC LOGISTIC assumes that the observations are independent. PROC CATMOD can handle repeated measures data but requires complete data. That is, all subjects must be measured at all times. The Generalized Estimating Equations (GEE) method implemented by the REPEATED statement in GENMOD does not require complete data, allowing missing measurements. PROC SURVEYLOGISTIC is for the case of survey data such as when you sample within strata of population.
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