Hi all, I'm running into an issue with my code. Briefly, I'm looking at how certain psychological constructs impact the effectiveness of a behavioral treatment. One of the treatments is time-out, where observers watch the kid for 30 seconds, and then record behavior for 10 seconds, and repeats this until the time-out ends. Naturally, it's an unbalanced dataset with some kids getting a few time-outs, and others getting many time-outs. I'm approaching it in a multi-level design: Level 1 = the interval where data are collected (ranges from 1-197) Level 2 = the unique ID # to identify the time-out (there are 545 unique time-outs) Level 3 = the unique ID # to identify the child So, interval data is nested in time-out, which is nested in child.  I'm running this in a stepwise approach. My first two models converged (see end of message). But model 3, where I want to include the full nesting term provides the following error:   1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; SYMBOLGEN: Macro variable _SASWSTEMP_ resolves to /home/u60849988/.sasstudio/.images/495a0ef2-2874-447e-a1c1-b64f1cce7501 SYMBOLGEN: Some characters in the above value which were subject to macro quoting have been unquoted for printing. SYMBOLGEN: Macro variable GRAPHINIT resolves to GOPTIONS RESET=ALL GSFNAME=_GSFNAME; 68 69 PROC GLIMMIX DATA=impstdmrpsra2 METHOD=LAPLACE; 70 CLASS MRPSID UnqTONum IntervalNum Meds TOTxCond; 71 MODEL Effective = meds TOTxCond week ptadhd ptcp pticu24 pticu24*TOTxCond / SOLUTION CL; 72 RANDOM intercept / SUBJECT=MRPSID; 73 RANDOM intercept / SUBJECT= UnqTONum(MRPSID); 74 RANDOM intercept / SUBJECT=IntervalNum(UnqTONum(MRPSID)) TYPE=UN; _ 22 200 ERROR 22-322: Syntax error, expecting one of the following: a name, ), *. ERROR 200-322: The symbol is not recognized and will be ignored.   Importantly, this occurs whether I use a singular random statement (i.e., RANDOM intercept / SUBJECT = IntervalNum(UnqTONum(MRPSID)) or multiple random statements as shown below. This is happening across both continuous and categorical variables. Any feedback or thoughts would be greatly appreciated. Thanks all.    Model 3 - not converging, receiving above error.  PROC GLIMMIX DATA=impstdmrpsra2 METHOD=LAPLACE; CLASS MRPSID UnqTONum IntervalNum Meds TOTxCond; MODEL Effective = meds TOTxCond week ptadhd ptcp pticu24 pticu24*TOTxCond / SOLUTION CL; RANDOM intercept / SUBJECT=MRPSID; RANDOM intercept / SUBJECT= UnqTONum(MRPSID); RANDOM intercept / SUBJECT=IntervalNum(UnqTONum(MRPSID)) TYPE=UN; options mprint mlogic symbolgen ; RUN;   Model 1 - converges. PROC GLIMMIX DATA=impstdmrpsra2 METHOD=LAPLACE; CLASS MRPSID Meds TOTxCond; MODEL Effective = meds TOTxCond week ptadhd ptcp pticu24 pticu24*TOTxCond / SOLUTION CL; RANDOM intercept / SUBJECT=MRPSID TYPE=UN; RUN;   Model 2 - converges. PROC GLIMMIX DATA=impstdmrpsra2 METHOD=LAPLACE; CLASS MRPSID UnqTONum Meds TOTxCond; MODEL Effective = meds TOTxCond week ptadhd ptcp pticu24 pticu24*TOTxCond / SOLUTION CL; RANDOM intercept / SUBJECT=UnqTONum(MRPSID) TYPE=UN; RUN;     ... View more

Hi all, I'm attempting to probe and plot interactions of my negative binomial that was analyzed using PROC GLIMMIX. Briefly, I'm examining the effect of two continuous variables. treatment week (ranging from 1-8) and affect, on various skewed and overdispersedoutcomes, along with several covariates (all mean-centered prior to analysis) and an offset to account for differences in exposure.    Across my models, I have two significant interactions but am having difficulty plotting them. I've been following the resources through UCLA (https://stats.oarc.ucla.edu/sas/seminars/analyzing-and-visualizing-interactions/). I'm able to successfully store my interaction. I then run an estimate *Week = 3 (centered mid-way pt); *CU = 0 (centered mean); Title 'Estimating AB using'; PROC PLM restore = INT; estimate 'pred behavior_aggress, cweek = 3, CU = 0' intercept 1 cweek 3 cpticu24 0 week*pticu24 0 / e; RUN; I am successfully able to test my simple slopes and find that the effect of time leads to less aggressive bx at lower levels of the affective trait. Here is where I run into trouble:   ***I'm using the mean centered values to create a dataset called timeout -11.64 = value of affect 1 SD below mean -.76 = value of mean affect 10.12 = value of affect 1 SD above mean; Title 'creating dataset to plot interactions'; data timeout; do ccu = -11.64, -.76, 10.12; do cweek = 1 to 8 by 0.1; output; end; end; run; ***I then set the layout of the plot; Title 'Setting layout of plot'; PROC PLM source = AGGBXINT; score data = timeout out = plotdata predicted = pred lclm = lower uclm = upper; RUN; I get the following error: At least one variable used in the model is not present or has incompatible type in the WORK.TIMEOUT score data set. All computed score variables are set to missing in WORK.PLOTDATA. Plotting the figure via SGPLOT doesn't happen because the LL/UL values haven't been computed. My variables appear to be formatted correctly and I don't have any missing data. Importantly, I get this same error when I use non-centered variables (i.e., no negative values for 1 SD below the mean).   I've scoured the internet with little luck and would love any/all feedback. I imagine I'm doing something terribly wrong.   Thanks.    ... View more

Hi all, I'm running ZINB via PROC GENMOD. Most models have run smoothly but I am running into an issue with one of my models. I'm using various child characteristics to predict zero-inflated behavioral and emotional count outcomes. Most IVs are continuous except for medication status (yes vs no). For one particular outcome, the logit portion of the model does not produce a p-value for my categorical variable, and produces the same value for the estimate and CIs; I'm not receiving any error messages. My online searches have yielded results predominately re: missing values.   Here is my code and output for the logit model. Any resources/thoughts would be greatly appreciated!   TITLE 'ZINB, DV = Anger Emo log'; PROC GENMOD DATA = datafile; CLASS Meds; MODEL Emotion_anger = meds time x1 x2 x3 / link = log dist = zinb offset = log_var; ZEROMODEL meds time x1 x2 x3; RUN;   Logit Model Output: Analysis Of Maximum Likelihood Zero Inflation Parameter EstimatesParameter  Estimate; SE; Wald 95% CI; Wald Chi-Square; p-value  Intercept; df = 1 -25.4554 0.7635 -26.9518 -23.9591 1111.72 <.0001 Meds yes; df = 0 24.4313 0.0000 24.4313 24.4313 . . Meds no; df = 0 0.0000 0.0000 0.0000 0.0000 . . time; df = 1 0.0725 0.1813 -0.2828 0.4279 0.16 0.6891 x1; df = 1 5.3979 1.9872 1.5031 9.2927 7.38 0.0066 x2; df = 1 4.1286 1.5235 1.1425 7.1146 7.34 0.0067 x3; df = 1 -0.1602 0.0610 -0.2798 -0.0406 6.89 0.0087   ... View more

Hi all.  I'm interested in analyzing how different affective traits predict behaviors during time-out (in the context of a treatment program) using GEE in SAS. - I have 49 youth, and my outcome is an overdispersed count variable with a negative binomial distribution. I also want to account for nesting of 3 levels: time-outs are nested within time [i.e., treatment week], and time is nested within child (ID). - It is an unbalanced dataset, such that some kids got 30 time-outs while others only got 1 or 2. - I've included # of timeouts within day as an offset term to account for the fact that youth with 1 time-out in a day are diff than youth with 4 time-outs in a day. - I'm also interested in various interactions but haven't included them yet because I want to ensure that my model is correct.   Here's my syntax so far with centered (c) predictors: Title "Model 1"; PROC GENMOD DATA = data.dataset; CLASS ID cweek; MODEL Behavior = meds age cx1 cx2 cx3 cweek / dist = negbin link = log offset = timeout_withindaycount; REPEATED SUBJECT = ID(cweek) / type = exch; RUN;   My repeated line is probably wrong, as my time variable isn't a categorical variable but produces individual effects for all 8 weeks. However, I'm unsure how to properly include nesting across the 3 levels if time isn't on the CLASS statement. Any feedback on where I've gone wrong would be great. Sorry for the elementary questions.  Thanks! ... View more