Recently in the SAS Community Library: SAS' @AndyRavenna publishes the second of two posts that uses statistics and machine-learning objects in SAS Visual Analytics to address real-world business problems.
Hi, I have a large set of users (internal users @saspw) and I'd like to give them the chance to change their internal password by themselves. They have an access to the stored process web app, and only that application. I am thinking of a Stored Process that they could use to change this password. Has someone already created such stored process? Thanks Cédric
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I have the attached data set and i am trying to get the output group by weekday , hour and sum energy with in that hour , for that weekday.
any help would be appreciated .
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I am running into a problem where I would like to duplicate data from one row that contains data to other rows with the same key value, but does not contain any additional data. However, ONLY conditioned if variable 3 or V3 is equal to "Cardio". For instance. I have a dataset that looks like this. The first two row contain the same key, but the second row does not contain any other information other than the key (no value for v1,v2, v3 etc.) Because the first row has "cardio" for V3, this row would need to be duplicated to the second row that does not contain the information. Now the second image is what I want the dataset to look like. How am I able to do that in SAS? Thanks in advance.
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I am running PROC MI for multiple imputation for a 5-level categorical variable, "gmfcs_final", which is the only variable in the dataset with missing values. The imputation phase works great (code under "STEP 1"). I then do the 2nd analysis phase (code under "STEP 2"). This analysis is focused purely on estimate the c-statistic with 95% CI for several models based on various covariate sets. This runs well and I output the c-stat and CI in the "auc_2" output file. For the 3rd step, I am unable to figure out what code to run to pool the c-stats and CI appropriately (Rubin's rules?). There seems to be code to get parameter estimates in the PROC MIANALYZE, but that is not the interest of this study. Any idea on the code using the PROC MIANALYZE or other code to get the appropriately pooled c-stats and CI? STEP 1 proc mi data=b seed=1305417 nimpute=65 out=mi_fcs; class gmfcs_final sex race ethnicity smoking_num ins yr_start WCI_score_1cl W1-W25 base_2-base_19 fx1_base fx2_base fx3_base fu_2_5yr_censrsn fu_3_5yr_censrsn fu_4_5yr_censrsn fu_5_5yr_censrsn fu_6_5yr_censrsn fu_7_5yr_censrsn fu_8_5yr_censrsn fu_9_5yr_censrsn fu_10_5yr_censrsn fu_11_5yr_censrsn fu_12_5yr_censrsn fu_13_5yr_censrsn fu_14_5yr_censrsn fu_15_5yr_censrsn fu_16_5yr_censrsn fu_17_5yr_censrsn fu_18_5yr_censrsn fu_19_5yr_censrsn fu_fx1_5yr_censrsn fu_fx2_5yr_censrsn fu_fx3_5yr_censrsn death_5yr_censrsn; var gmfcs_final age sex race ethnicity smoking_num ins yr_start WCI_score_1cl W1-W25 base_2-base_19 fx1_base fx2_base fx3_base fu_2_5yr_censrsn fu_3_5yr_censrsn fu_4_5yr_censrsn fu_5_5yr_censrsn fu_6_5yr_censrsn fu_7_5yr_censrsn fu_8_5yr_censrsn fu_9_5yr_censrsn fu_10_5yr_censrsn fu_11_5yr_censrsn fu_12_5yr_censrsn fu_13_5yr_censrsn fu_14_5yr_censrsn fu_15_5yr_censrsn fu_16_5yr_censrsn fu_17_5yr_censrsn fu_18_5yr_censrsn fu_19_5yr_censrsn fu_fx1_5yr_censrsn fu_fx2_5yr_censrsn fu_fx3_5yr_censrsn death_5yr_censrsn; fcs discrim(gmfcs_final = age sex race ethnicity smoking_num ins yr_start WCI_score_1cl W1-W25 base_2-base_19 fx1_base fx2_base fx3_base fu_2_5yr_censrsn fu_3_5yr_censrsn fu_4_5yr_censrsn fu_5_5yr_censrsn fu_6_5yr_censrsn fu_7_5yr_censrsn fu_8_5yr_censrsn fu_9_5yr_censrsn fu_10_5yr_censrsn fu_11_5yr_censrsn fu_12_5yr_censrsn fu_13_5yr_censrsn fu_14_5yr_censrsn fu_15_5yr_censrsn fu_16_5yr_censrsn fu_17_5yr_censrsn fu_18_5yr_censrsn fu_19_5yr_censrsn fu_fx1_5yr_censrsn fu_fx2_5yr_censrsn fu_fx3_5yr_censrsn death_5yr_censrsn /classeffects=include) nbiter=100; run; STEP 2 proc logistic data=mi_fcs plots(only)=roc; class sex race3 smoking_num ins2 yr_start_cat W24 W25 WCI_score_1cl gmfcs_final; model fu_2_5yr(event='1')=age sex race3 smoking_num ins2 yr_start_cat WCI_score_1cl gmfcs_final / nofit; roc 'Base model' age sex race3 smoking_num ins2 yr_start_cat; roc 'GMFCS only' gmfcs_final; roc 'WCI only' WCI_score_1cl; roc 'Base+GMFCS' gmfcs_final age sex race3 smoking_num ins2 yr_start_cat; roc 'Base+WCI' WCI_score_1cl age sex race3 smoking_num ins2 yr_start_cat; ods output rocassociation=auc_2; by _imputation_; run;
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