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palolix

Dear SAS Community, I was looking for a way of visualizing the association between different categorical variables (nominal and ordinal variables). Since a heatmap will probably not work for categorical variables I think that a correspondence analysis should be a good option. Is it ok to include nominal (more than two levels) and also ordinal variables together in a correspondence analysis? I would greatly appreciate your feedback!

YannRC

Hi, I am a beginner in Regex and it's very difficult to understand some errors. I want to find 30 kms or 30 km, or 30 kilometers REGEX: [0-9][0-9] km(s)?. I Try with that only for km or kms but I have always a error with a escape character and I don't understand when I have to add this one.

OneEyedKing

I am seeking to use PROC ENTROPY to model a cost function, but I can see evidence of heteroskedasticity in the omnibus studentized residual plot that I hope to remediate. Ideally, I would like to conform to the usual "rules of road" in using the variance (i.e. squared residuals) but as I am not as familiar with PROC ENTROPY I don't want to blindly apply something from OLS that would not be appropriate under GME. For example, in reviewing the documentation for WEIGHT option in PROC ENTROPY: The regressors and the dependent variables are multiplied by the square root of the weight variable to form the weighted matrix and the weighted dependent variable. This differs from the implementation of the WEIGHT option in PROC GLM: If the weights for the observations are proportional to the reciprocals of the error variances, then the weighted least squares estimates are best linear unbiased estimators (BLUE). I do not understand why there are two different implementations of weighting between ENTROPY and GLM, thus this question. Thanks for any insights you can offer me regarding this issue.

Sajid01

I want to obtain the sql_id for query that is sent to the oracle database using Proc Sql. Wondering if there is a way I can obtain it.

nianhui

Hello, I'm geocoding <100 addresses using PROC GEOCODE with downloaded 2024 Steet Lookup Data for 9.4. The output showed good street match, but many of the coordinates (X and Y) are off, way off. Here are two output examples (matched addressed are not exactly the input addresses) Matched address: "3003 Sevierville Rd, Maryville, TN 37804" with X=-92.68680269 and Y=34.458537714 (This coordinates fall into Arkansas) Matched address: "206 Debbie Ann Dr, Leander, TX 78641" with X=-80.1951071 and Y=39.556943449 (This coordinates fall into West Virginia) I wonder if anyone has used these lookup data and run into similar problems. Thanks!