Hi, How do I fill color under density curves? Here are my sample data and sgpanel procedure. TIA! /* Sample data */ data mydata; input group $ value; datalines; A 10 A 12 A 13 B 9 B 11 B 14 C 8 C 10 C 11 ; run; /* Creating the panelled density plot */ proc sgpanel data=mydata; panelby group / layout=rowlattice columns=1 novarname; density value; run;
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In this article, we will look at creating custom SAS Viya deployment topologies, realizing your workload placement plan. In doing this we will look at a couple of examples as a way of sharing some configuration specifics of using custom labels and taints.
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I am a novice using macros. I want to perform mediation analysis and want to use the PROCESS macro by A. Hayes. While using the macro I get the error: use &data; ERROR 22-322: Expecting a name. Line generated by the invoked macro "PROCESS". 43 use &data; ERROR 22-322: Expecting a name. how do I solve this? Thanks, Marij
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I have an admit date variable (admitdt) that needs to be brought in to define a few month and week variables but these have to be done in a separate step. Is there a way to create a macro in one data step that can be used in several? Do I just do a LET statement The admitdt format is mmddyy10.
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Hi: I am working on a study. It has been planned to do multiple imputation for missing values related to the primary endpoint by an agency's requirements. I have read many materials online and have some ideas. But, I am still not sure. Study information: The study's indication is Epilepsy. Each patient is given a diary and they are supposed to record how many seizures they expeirenced each day during the study period (DB period is ~ 85 days). As you can image, some patients may forget to record their seizures on some days and some patients may discontinue from the study before the Day 85. So there are missing values for seizure counts on some days for some subjects. My questions: 1. Our missing type is considered as 'missing as random' and so this procedure(Proc MI) can be used, correct? 2. Since our missing data is only in one variable, i.e., seizure count, so I think I should NOT use the methods in SAS documentation (Imputation Methods, Table 5) with 'monotone', correct? 3. Since our data is 'seizure count', which should follow poission distribution (correct?), not normal, I should NOT use methods with 'MCMC' since MCMC method is based on the assumption of multivariate normal distribution (MVN) for variables, correct? 4. Then I thought I should use FCS, fcs reg, or fcs regpmm. I read SAS documentation, it has "The predictive mean matching method ensures that imputed values are plausible; it might be more appropriate than the regression method if the normality assumption is violated (Horton and Lipsitz 2001, p. 246)." So I thought I should use 'fcs regpmm'. I also tried 'fcs reg', the imputed values gives non-integer, a number with decimal. It seems it does not fit my case. Our seizure is an count; so it should be an interger. If I use 'fcs regpmm', the imputed values are integers. 5. If using 'fcs regpmm' is correct for my case, what number of 'k' (SAS option with 'fcs regpmm' option) should I pick? Here is the code I use. proc mi data = post nimpute = 25 out = post_mi seed = 54321 noprint; by subjid; var qsdy count;' fcs regpmm (/k = 5); run; Note: 'qsdy' is the study Day variable; it is from Day 1 till Day 85. 'count' is seizure count for each day. There are missings in this variable. Note: since the imputation is by subjid, so covariates such as age, treatment, etc, are not needed (no change for an individual), correct? If any detailed information is needed for this discussion, please ask me. Thanks a lot in advance. Xiaoshu
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