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SASuser11466
‎09-17-2015
  • 12 Posts
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Ods Graphics Interaction Plot

by Calcite | Level 5 in SAS Programming
‎03-22-2015 06:13 PM
‎03-22-2015 06:13 PM
Hello everyone: I understand that when using Proc GLM in 9.3, you can use ods graphics on to create a default interaction graph. I am using the following simple code to run an interaction between gender and BMI odsgraphics on; proc GLM data=work.BMI; class FEMALE CIT_RACE MARSTAT EDUCATS WORKING; title'Model 4: OLR Prediciting Positive Mental Health'; title2'Interatcion BMI*FEMALE'; model POSMEN =  BMI FEMALE H001 CIT_RACE MARSTAT LOGINC EDUCATS WORKING CHILDREN BMI*FEMALE / SOLUTION; RUN;QUIT; ods graphics off; when I run this I get all the standard output in ODS format (set to a browser) but I do not get a graph. Any assistance greatly appreciated. Thanks! ... View more

Re: Proc Logistic with PROC MI and PROC MIANALYZE: Class Variable Prob...

by Calcite | Level 5 in Statistical Procedures
‎11-28-2012 01:49 PM
‎11-28-2012 01:49 PM
Thanks so much Steve! I typically code dummy variables numerically using the highest as the omitted (e.g. 0="African American"; 1="Hispanic"; 2="Non-Hispanic White" as reference). I will add the statement your provided above as soon as I get back to the office. Thanks Again! Gabriel ... View more

Proc Logistic with PROC MI and PROC MIANALYZE: Class Variable Problem

by Calcite | Level 5 in Statistical Procedures
‎11-27-2012 08:37 PM
‎11-27-2012 08:37 PM
Hello SAS communities: I am trying to carry out a multiple imputation procedure using PROC MI; PROC MIANALYZE as well as the experimental FCS option in SAS 9.3.  The missings are random NOT monotonic. I have a dataset (survey data) with approximate N=4,800 observations. The dept variable DTHPEN is support for capital punishment (0=No; 1=Yes), I am trying to predict support for death penalty. Independent vars are GENDER (CLASS Variable coded 0=Male; 1=Female); church attendance (ordinal variable 0-7); and number of children (numerical, actual number of children). Weight variable is VAR5416; I plan to add more variables into the equation but am trying to use PROC MI and MIANALYZE. I want to work with a smaller number of variables before moving on. I have pasted the code I am using below. Everything seems to work fine for the PROC MI and PROC logistic runs (Step 1 and Step 2) but the PROC MIANALYZE procedure gives me the following warning in the SAS LOG: "ERROR: Variable GENDER is not in the PARMS= data set. I assume I am having some problem with gender as a CLASS variable? Any assistance is greatly appreciated. Thanks! Gabriel /*STEP 1: Enter data for imputation*/ procmi data=WORK.CAPWHT nimpute=25out=helpmi25fcs; class DTHPEN GENDER; FCS logistic(DTHPEN = ATTNDC CHLDRN) logistic (GENDER); var ATTNDC CHLDRN GENDER DTHPEN; run; /*STEP 2: Code for the logistic regression*/ proclogistic data = helpmi25fcs descending; class DTHPEN GENDER; model DTHPEN= ATTNDC CHLDRN GENDER / covb expb; by _Imputation_; weight VAR5416; odsoutput ParameterEstimates=helpmipefcs covb=helpmicovbfcs;RUN; /*step 3 MIANALYZE procedure*/ title"proc Logistic imputations for relig variables"; title2"Whites only 25 Iterations"; procmianalyze parms=helpmipefcs covb(effectvar=stacking)=helpmicovbfcs; class GENDER; modeleffects intercept ATTNDC CHLDRN GENDER; run;quit; ... View more

Re: Zero Inflated Binomial or Zero-Inflated Poisson

by Calcite | Level 5 in Statistical Procedures
‎07-25-2012 10:19 PM
‎07-25-2012 10:19 PM
Sorry for my delayed response Dorata_j. I've had a A very busy few days. Thanks for getting back to me so quickly. PG Stats, Thanks to you as well!! After your note, I looked around and found something telling me I need 9.22 or 9.3 for PROC COUNTREG to take the CLASS statement. Thanks for that tip on upgrading. I have already passed along a request to our admin regards 9.3. many Thanks all! Gabriel ... View more

Zero Inflated Binomial or Zero-Inflated Poisson

by Calcite | Level 5 in Statistical Procedures
‎07-23-2012 03:21 PM
‎07-23-2012 03:21 PM
Hello everyone, I have a question about Zero Inflated Models. We have a very standard survey data set. Our main interest is predicting volunteer hours and "amount of dollars contributed voluntarily." Both response variables have greater variance than the mean and each has an accompanying variable that asks if the respondent was asked to either "give money" or "give time." So, we have lots of zeroes (people could have said "no" when asked to volunteer or give money) and the distribution includes excessive zeroes. (e.g. zero hours volunteered and/or zero dollars contributed). We have standard predictor variables, some are ordinal (e.g. age, income, church attendance) others are categorical/class variables. Here are a few questions: 1. Any reccommendations for either inflated negative binomial or zero inflated Poisson models 2. Does anyone know how PROC countreg handles class variables? I am running SAS 9.2 and PROC countreg does not seem to handle the class statement. 3. Does anyone have a brief explanation of how to use the zeromodel command in countreg and what is typically an appropriate variable for the zeromodel portion of the model? 4. If anyone has any examples as code or responses to the above, we would be very grateful. Thanks, Gabriel ... View more

GLIMMIX Output Question

by Calcite | Level 5 in Statistical Procedures
‎08-26-2011 03:37 PM
‎08-26-2011 03:37 PM
As with the previous response, THANKS ! so much. Everyone's assistance greatly appreciated. ... View more

GLIMMIX Output Question

by Calcite | Level 5 in Statistical Procedures
‎08-26-2011 03:36 PM
‎08-26-2011 03:36 PM
Thanks so much for the reply. SAS ODS is a very powerful tool, your insights are greatlyappreciated!   ... View more

GLIMMIX Output Question

by Calcite | Level 5 in Statistical Procedures
‎08-25-2011 05:39 PM
‎08-25-2011 05:39 PM
Thanks Again Reeza but I did add odds ratio and with/without issue remains the same. All output, (even fixed effects) are displayed in matrix form as shown below. Just wish I'd get the standard results table as with other regression commands (Proc logistic, GLM, etc.) Thanks again for taking the time. I apperciate it!                                                  Solutions for Fixed Effects            Measure            of            Perfect            Belief    Binary                             Respondent'            vs.       Religious  Religious   Respondent  s Age: 3     Marital   Education  Income                Standard Effect     Skeptics  Salience   Person?     Gender      Categories   Status    level      Categories  Estimate     Error     DF Intercept                                                                                               0.5789    0.2787      5 PERFBEL    aSkeptic                                                                                     0.1944   0.07643      5 PERFBEL    bPerfBel                                                                                          0         .      . BRELSAL              aVeryImp                                                                          -0.1164    0.1538      5 BRELSAL              bNotImp                                                                                 0         .      . RELATTEND                                                                                             -0.02074  0.008409  10416 RELPER                          aReligious                                                             0.08591   0.06756      5 RELPER                          bNon-Relig                                                                   0         .      . GENDER                                      aMALE                                                      -0.3295   0.04662      5 GENDER                                      bFEMALE                                                          0         .      . AGECATS                                                 a15-29                                        -0.01795   0.05986     10 AGECATS                                                 b>50                                          0.001864   0.06272     10 AGECATS                                                 c30-49                                               0         .      . MARCATS                                                              aMarried                          0.02945   0.06712     10 MARCATS                                                              bOthr                              0.2188    0.1149     10 MARCATS                                                              cNvrMar                                 0         .      . EDUCATS                                                                        aLow                   -0.08205   0.05243     10 EDUCATS                                                                        bHigh                  -0.06086   0.06146     10 EDUCATS                                                                        cMedium                       0         .      . INCATS                                                                                    aLow          0.1000   0.04951     10 INCATS                                                                                    bHigh         0.1094   0.05678     10 INCATS                                                                                    cMedium            0         .      . SOCLASS                                                                                                0.06797   0.02429  10416 CHILD                                                                                                 -0.02411   0.01297  10416 ... View more

GLIMMIX Output Question

by Calcite | Level 5 in Statistical Procedures
‎08-24-2011 02:12 PM
‎08-24-2011 02:12 PM
Thank for for getting back to me.  The ordinal variable is not in the class statement. I can see the results but everything is in a matrix format that makes interpretation difficult almost like SAS is trying to contrast every variable against each other variable and not presenting fixed effects and odds ratios in one table as with Proc Logistic. If it helps, code is below. (yes, I am a sociologist, running quite a few variables in the model) The outcome is binary (0/1) and LOGPPP is a second level variable for country purchasing power parity (logged), along with country for each country,  RELATTEND, SOCLASS, and CHILD are ordinal variables. All others are dummy coded categorical variables.  Again, thanks for the response. I tried the HLM software, did not care for it, and just started with Proc GLIMMIX.  proc GLIMMIX data  = WORK.NEWISLAM; class COUNTRY FREEBINARY PERFBEL BRELSAL RELPER GENDER AGECATS MARCATS EDUCATS INCATS; model NDCHLD (descending) = LOGPPP FREEBINAR PERFBEL BRELSAL RELATTEND RELPER GENDER AGECATS MARCATS EDUCATS INCATS SOCLASS CHILD / dist=binary link=logit ddfm=bw solution; random int / subject=COUNTRY solution; RUN; QUIT; ... View more

GLIMMIX Output Question

by Calcite | Level 5 in Statistical Procedures
‎08-23-2011 06:29 PM
‎08-23-2011 06:29 PM
Hello everyone: I am a novice to multi level modeling and have a program running using Proc GLIMMIX. Its a two level model, relatively straightforward, several covariates (from survey data), country level is the 2nd level. Things look ok however, I have requested odds ratios and am getting strange output. Instead of the nice, clean usual output screen you get from say, PROC LOGISTIC, I seem to be getting a matrix that makes it hard to tell the odds ratio for each variable. I also have a few ordinal variables in the model and it seems SAS is giving me an odds ratio for EACH level of the variable (unlike PROC LOGISTIC that gives one odds ratio for each unit increase in the ordinal variable)? Any ideas or easy-to-read sources for a new GLIMMIX user? Thanks ... View more
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