Thoughts of a Crime Show Junkie: Inadmissible Evidence
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Hi all,
At https://developer.sas.com/apis/rest/v3.5/#filters there is supposed to be a link that explains filter expressions:
For a complete description of filter expressions, see the Filtering reference.
However, when clicking this link (on the word "Filtering") I do not see any such page. It looks like this "got lost" during the switch to the new developers.sas.com web site.
Anyone knows where can I find the doc about API filter expressions?
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Hi When dealing with SAS alerts, I make a description in notes before closing the alert. I always use the same sentence in the end like "Nothing suspicious found" Is there a way to make a shortcut to this text or like in word make an autotext? In stead of typing it in everytime, I would like to press fx "ALT+I"
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I constructed a mmrm model as below: proc mixed data = have;
class id treatment week strata;
model chg = base treatment week treatment*week treatment*strata/ ddfm = KR;
repeated week/ subject = id type = UN;
lsmeans treatment*week/ cl alpha = 0.05 diff
ods output Tests3=tests3 lsmeans=lsmeans diffs=diffs;
run; (1) id: represents the patient id; (2) treatment: contains 2 group, "drug" and "placebo"; (3) chg: change from baseline of hba1c; (4) base: the value of hba1c at baseline; (5) week: contains 3 levels: "week8", "week16" and "week24" (6 )strata: stratification based on the median of baseline hba1c, ie group1=patients of those who have baseline hba1c <= median of baseline hba1c; group2=patients of those who have baseline hba1c > median of baseline hba1c. In general, I'd like to know how to the test the interaction between strata and treatment groups. I am not sure if I constructed the model in a right way because I looked at the results (as below), the values of Diffs in LS means were pretty close between strata, but p for interaction was significant. (The Diffs in LS means were got from the output file diffs, and the p for interaction were got from the output file test3.) I also checked the Diffs in LS means of week8 and week16, the diffs in LS means were all pretty close between 2 strata, but the p for interaction were all significant (p-value < 0.0001). So I am not sure if I interpret the result in the right way. What data should look quite different if the p for interaction is statistically significant? Thanks.
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DATA ALT;
SET RCA.CLOSING_STATUS_TEST;
ARRAY COLS[*] _CHARACTER_;
ARRAY COLS_[*] _CHARACTER_;
DO I = 1 TO DIM(COLS);
IF MOD(I, 2) = 1 THEN
COLS_[(I+1)/2] = COLS[I];
END;
DROP I;
RUN;
Hi, I have been trying to select the alternate columns. If I am using this code then my third column's data shifts to second column and second's values are disappearing. I want to select alternate columns from my data set and my column names are in following: LOAN_NO, PRODUCT, FINAL_STATUS_05MAY24,DUE_N_TAG_05MAY24, FINAL_STATUS_06MAY24,DUE_N_TAG_06MAY24, FINAL_STATUS_07MAY24,DUE_N_TAG_07MAY24,... and so on. I want to create a table selecting columns:-
LOAN_NO, FINAL_STATUS_05MAY24, FINAL_STATUS_06MAY24, FINAL_STATUS_07MAY24... and so on. If there is any alternate approach for making the selection, please advise me or please help with the given array code.
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I have a data set with multiple variables (columns). I'm trying to get the top 10 values of each variable. However, I would like the output to be in a new dataset with a new variable called: variable_names. The row shows the top 10 values from smallest to largest. The data below is just an example. I have more than 4 input variables. Here, I am just looking at the top 3. For a bigger data set, I may look at the top 10. data example; input a b c d; datalines; 100 120 123 140 12 23 43 42 12 12 23 23 5 7 5 2 80 88 98 2 3 4 101 4 ; %let nTop = 3; proc univariate data = new_data NExtrObs=&nTop; ods select ExtremeObs; run; The proc univariate gives a separate output for each. The UNIVARIATE Procedure Variable: a Extreme Observations Lowest Highest Value Obs Value Obs 3 6 12 3 5 4 80 5 12 3 100 1 I would like to create a new data set where output with column names: Variables and the top values. For example, Variables Top1 Top2 Top3 a 12 80 100 b 23 88 120 c 5 6 1
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