About khollid

khollid · ‎05-23-2017

Yes. So for me it was just 1 variable that was an issue, but the way the warning works is to list every variable. When I took out the 1 problematic variable, the warnings all went away. In my case, one variable was perfectly predicted by the other two as it is var1=var2-var3. When I took var1 out of the list, the warnings cleared up. I would try to figure out if any of your variables can be perfectly predicted by some combination of other varibles, realizing that the warnings don't actually mean that every variable is the issue. Then you can take out the problematic variable and run the imputation. After the imputation finished, I then calculated an imputed var1 by having SAS calculate it from the imputed var2 and var3 for any initial missing values of var1. Hope this helps!

khollid · ‎02-09-2017

I am trying to complete a multiple imputation for a dataset that has participants at 3 visits (data in short format). After setting up and running the basic code in SAS 9.4, I receive the warning "An effect for variable X is a linear combination of other effects. The coefficient of the effect will be set to zero in the imputation." for almost all of my variables (but they aren't all linear combinations). Any ideas why this could be happening? We have a lot of variables we are trying to impute in a single dataset, although the eventual models will only focus on a subset of the variables. The 1, 2, and 3 at the very end of the variable names represent visit # for variables that change over time. For pollutant and weather variables, nomenclature is name of pollutant/weather characteristic + MN (for mean) + 2, 7, 28 or 365 for the averaging period of days over which the mean is calculated. For our actual analyses after the imputation, we would only be using one set of averaging times and one pollutant exposure in one model (i.e. we might have a 2-day mean CO with the 2-day mean temperature, pressure, and dewpoint) along with the other variables that don't end in the 2, 7, 28, or 365. Could the number of variables be causing this? I'm using a book called "Multiple Imputation of Missing Data Using SAS" that suggested this approach of putting the data in short form would suffice for the longitudinal setting, but maybe this dataset is too complex? proc mi data=data_short nimpute=10 seed=270 out=data_impute; class hrtarm dmarm cadarm hseduc ethnic smk_statusn1 smk_statusn2 smk_statusn3 alc_statusn1 alc_statusn2 alc_statusn3 center3dn1; fcs logistic (hseduc smk_statusn1 smk_statusn2 smk_statusn3 alc_statusn1 alc_statusn2 alc_statusn3) regression (texpwkn1 texpwkn2 texpwkn3 bmin1 bmin2 bmin3 tempmn2n1 tempmn2n2 tempmn2n3 tempmn7n1 tempmn7n2 tempmn7n3 tempmn28n1 tempmn28n2 tempmn28n3 tempmn365n1 tempmn365n2 tempmn365n3 dewpmn2n1 dewpmn2n2 dewpmn2n3 dewpmn7n1 dewpmn7n2 dewpmn7n3 dewpmn28n1 dewpmn28n2 dewpmn28n3 dewpmn365n1 dewpmn365n2 dewpmn365n3 premn2n1 premn2n2 premn2n3 premn7n1 premn7n2 premn7n3 premn28n1 premn28n2 premn28n3 premn365n1 premn365n2 premn365n3 z_score_sumn1 z_score_sumn2 z_score_sumn3 PM10MNMOn1 PM10MNMOn2 PM10MNYRn1 PM10MNYRn2 PM25MNMOn1 PM25MNMOn2 PM25MNYRn1 PM25MNYRn2 PMcMNMOn1 PMcMNMOn2 PMcMNYRn1 PMcMNYRn2 COMN2n1 COMN2n2 COMN2n3 COMN7n1 COMN7n2 COMN7n3 COMN28n1 COMN28n2 COMN28n3 COMN365n1 COMN365n2 COMN365n3 NO2MN2n1 NO2MN2n2 NO2MN2n3 NO2MN7n1 NO2MN7n2 NO2MN7n3 NO2MN28n1 NO2MN28n2 NO2MN28n3 NO2MN365n1 NO2MN365n2 NO2MN365n3 NOXMN2n1 NOXMN2n2 NOXMN2n3 NOXMN7n1 NOXMN7n2 NOXMN7n3 NOXMN28n1 NOXMN28n2 NOXMN28n3 NOXMN365n1 NOXMN365n2 NOXMN365n3 O3MN2n1 O3MN2n2 O3MN2n3 O3MN7n1 O3MN7n2 O3MN7n3 O3MN28n1 O3MN28n2 O3MN28n3 O3MN365n1 O3MN365n2 O3MN365n3 PM10MN2n1 PM10MN2n2 PM10MN2n3 PM10MN7n1 PM10MN7n2 PM10MN7n3 PM10MN28n1 PM10MN28n2 PM10MN28n3 PM10MN365n1 PM10MN365n2 PM10MN365n3 PM25MN2n2 PM25MN2n3 PM25MN7n2 PM25MN7n3 PM25MN28n2 PM25MN28n3 PM25MN365n2 PM25MN365n3 PMcMN2n2 PMcMN2n3 PMcMN7n2 PMcMN7n3 PMcMN28n2 PMcMN28n3 PMcMN365n2 PMcMN365n3 SO2MN2n1 SO2MN2n2 SO2MN2n3 SO2MN7n1 SO2MN7n2 SO2MN7n3 SO2MN28n1 SO2MN28n2 SO2MN28n3 SO2MN365n1 SO2MN365n2 SO2MN365n3) ; var hrtarm cadarm dmarm ageDSRn1 ethnic CENTER3Dn1 CENTER3Dn2 CENTER3Dn3 q2n1 q3n1 q4n1 bmin1 hseduc alc_statusn1 smk_statusn1 SO2MN28n1 SO2MN7n1 SO2MN2n1 PM10MN28n1 PM10MN7n1 PM10MN2n1 O3MN28n1 O3MN7n1 O3MN2n1 NOXMN28n1 NOXMN7n1 NOXMN2n1 NO2MN28n1 NO2MN7n1 NO2MN2n1 COMN28n1 COMN7n1 COMN2n1 PMcMNYRn1 PMcMNMOn1 PM25MNYRn1 PM25MNMOn1 PM10MNYRn1 PM10MNMOn1 dewpmn28n1 dewpmn2n1 tempmn28n1 tempmn7n1 tempmn2n1 dewpmn7n1 SO2MN365n1 PM10MN365n1 O3MN365n1 NOXMN365n1 NO2MN365n1 COMN365n1 tempmn365n1 dewpmn365n1 SO2MN365n2 SO2MN28n2 SO2MN7n2 SO2MN2n2 PM10MN365n2 PM10MN28n2 PM10MN7n2 PM10MN2n2 O3MN365n2 O3MN28n2 O3MN7n2 O3MN2n2 NOXMN365n2 NOXMN28n2 NOXMN7n2 NOXMN2n2 NO2MN365n2 NO2MN28n2 NO2MN7n2 NO2MN2n2 COMN365n2 COMN28n2 COMN7n2 COMN2n2 q4n2 q3n2 q2n2 tempmn365n2 tempmn28n2 tempmn7n2 tempmn2n2 ageDSRn2 PMcMNYRn2 PMcMNMOn2 PM25MNYRn2 PM25MNMOn2 PM10MNYRn2 PM10MNMOn2 dewpmn365n2 dewpmn28n2 dewpmn7n2 z_score_sumn2 dewpmn2n2 z_score_sumn1 premn28n2 premn28n1 premn7n1 premn7n2 premn2n2 premn2n1 premn365n2 texpwkn2 premn365n1 alc_statusn2 texpwkn1 smk_statusn2 bmin2 PMcMN2n2 PM25MN2n2 PMcMN7n2 PM25MN7n2 PMcMN28n2 PM25MN28n2 PMcMN365n2 PM25MN365n2 q4n3 q3n3 q2n3 ageDSRn3 z_score_sumn3 tempmn28n3 tempmn7n3 tempmn2n3 dewpmn28n3 dewpmn7n3 dewpmn2n3 tempmn365n3 dewpmn365n3 SO2MN365n3 PMcMN365n3 PM25MN365n3 PM10MN365n3 O3MN365n3 NOXMN365n3 NO2MN365n3 COMN365n3 premn28n3 premn7n3 premn2n3 premn365n3 SO2MN28n3 PMcMN28n3 PM25MN28n3 PM10MN28n3 O3MN28n3 NOXMN28n3 NO2MN28n3 COMN28n3 SO2MN7n3 SO2MN2n3 PMcMN7n3 PMcMN2n3 PM25MN7n3 PM25MN2n3 PM10MN7n3 PM10MN2n3 O3MN7n3 O3MN2n3 NOXMN7n3 NOXMN2n3 NO2MN7n3 NO2MN2n3 COMN7n3 COMN2n3 alc_statusn3 texpwkn3 smk_statusn3 bmin3; run; Thanks for any ideas!

khollid · ‎03-16-2016

I ended up changing the data structure so that I could estimate this aim with minutes of activity as the outcome instead of having a nominal outcome, and I was able to get those models to run. However, I'm now working on another aim and I'm back to needing to work with the nominal outcome again. I have tried a lot of things but can't get the model to work. In this case, I again have repeated observations of physical activity locations for participants. Each observation has an outcome of a categorical location of physical activity. I am wanting to test for differences in activity locations by various factors (e.g. gender) accounting in some way for the correlation between repeated observations for individuals. At this point we are ignoring all of the other nesting, we're just trying to get this basic control for repeated measures across ID included in the model. My code thus far is: proc glimmix data=mvpa method=laplace; class ID maincat; model maincat (REF="0")=gender /dist=MULT link=glogit solution cl ; RANDOM intercept / SUBJECT=ID type=vc group=maincat; covtest/wald; title "Main*GLogisticTest"; run; Currently maincat is coded 0-9. I previously had it as a character variable (e.g. "home" "road") but was told in a SAS help session at school that I needed to change it to 0-9. Is that correct? If so am I supposed to sort by maincat before running the model? I'm also unsure about whether or not maincat should be in the class statement and about method=laplace. I thought I needed method=laplace, but the help session suggested not specifying the method=. I keep getting issues of either not having a valid objective function or insufficient memory. I tried it on the cluster and SAS returned an error of "Model is too large to be fit by PROC GLIMMIX in a reasonable amount of time on this system. Consider changing your model." I did try running binary models to get starting cov parms but inputting those parms received an error that the parms weren't feasible. There are about 220 IDs, 128,000 observations (max obs per subject 2800). Any suggestions?

khollid · ‎10-01-2015

Thanks for your response. I'm glad to know it is at least technically possible and how to do it. I think I can change the dataset to have the minute not be 1-1440. I don't have outcomes for every minute of the day, so I will pare it down to the ones that do (and time of day isn't of concern for the analysis). I will also try to see if I can run it on the cluster in the next couple of days to see if that helps with memory (I'll have to sign up for access first). I'll check back in and let you know how it goes once I've tried all my options. Thanks again!

khollid · ‎09-29-2015

Hi. I am having trouble identifying the best SAS PROC for my analytic question. I would ike to calculate an ICC for data that have a nominal (i.e. categorical but not ordinal) outcome. The data is hierarchical, falling into four levels, as well as repeated over time. I have not been able to find something that can do all of these things. Glimmix seems to handle nominal hierarchical data, but I have not found a way to add in the repeated part. Is this possible? Should I use a different proc? My code thus far (although it is not all correct) is below. The data structure is "round" nested within "day" nested within participant ("ID") nested within "state". The outcome is the nominal variable "main" and it is repeated over "minute" (1-1440 for each day). The below code does not work with the last random statement included (returns error that Glimmix cannot have r-side random effects with multinomial distribution). I am also not confident that the overall set up is correct. I would like to calculate the ICC for the variation due to day. This is the only parameter I am interested in. Any suggestions are appreciated. PROC GLIMMIX DATA=overall METHOD=RSPL; CLASS state ID day round minute main; MODEL main (order=freq ref=first)= / DIST=multinomial LINK=GLOGIT; RANDOM INTERCEPT / SUBJECT=state group=main TYPE=VC ; RANDOM INTERCEPT / SUBJECT=ID (state) group=main TYPE=VC ; RANDOM INTERCEPT / SUBJECT=day (ID state) group=main TYPE=VC ; RANDOM INTERCEPT / SUBJECT=round (day ID state) group=main TYPE=VC ; RANDOM minute/ SUBJECT=round(day ID state) group=main2 type=VC residual;

khollid · ‎09-26-2014

I changed my mind. I thought more about what you said about changing the definition. I think it does make sense to include any point that is part of a potential run, but just not double counting the overlap points. So all I need to do is expand the dataset produced by this code to the minute level and then delete the duplicates to yield any point that is part of a run that meets the definition, which I know how to do. Thanks!

khollid · ‎09-19-2014

Thank you, this looks very promising. I have passed it on to the programmer I am working with-she hasn't used hash objects in SAS before so hopefully this will be a fresh perspective for her. For your question, yes I would want to get rid of overlapping bits (edit: not necessarily get rid of, rather only include them in one run. The run in which the points are not included would need to fit the conditions on its own without those points as well). I need to isolate independent bouts of activity. My overall goal is to describe where these bouts of activity are occurring, so if points are double counted it will alter that distribution.

khollid · ‎09-17-2014

I do have the date and time information as a SAS datetime variable, although thus far I have created a variable for minutes 1-1440 for each day and have been using that as my sequence number. 10 minutes is the minimum, more than 10 minutes are acceptable and expected. There is no set maximum for the length of the string. However do note that the 0/1 data is a measure of physical activity intensity for a person at that minute, so I don't observe it continuing for many hours in the data. Most strings are 10-20 minutes long. A handful approach a couple of hours. Thanks for the example-I realize I forogt one rule. The string must start and end with a 1. However, your example of ambigous strings is still relevant. This issue is why I said there might not be a 100% accurate/right way to write the program. Ideally, I would like the program to maximize the number of points flagged in cases of ambiguity. Otherwise, I think the rule would just have to be an administrative decision to include the first such points. E.g. 1 0 0 1 1 1 1 1 1 1 0 0 1 1 would mark the last 11 points and leave off the first 3 (because 11>10) but 1 0 0 1 1 1 1 1 1 1 0 0 1 would mark the first 10 observations and leave off the last 3 since the first and last 10 yield the same number of points. In terms of the data, it is mostly 0s. There are blips of 1s here and there and then the small sections of more dense 1s that I am trying to pick out. The dataset is quite large, so identifying them by hand is very cumbersome. Below is an example string. The five zeros at the beginning and end set the section off from the rest of the data for the day. I think the 2 underlined sections maximize the number of points flagged while still being less than or equal to 20% 0's. The two sections are split because including those middle 2 zeros would bring the string above 20% zero. 0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0

khollid · ‎09-17-2014

Hi, I have been working with a programmer for several months trying to find an acceptable solution to a programming issue with no luck. Basically, I have a SAS dataset that has a binary variable (0,1) with one record for each minute of the day. I would like to find periods of at least 10 minutes of the day where there is a specific density of 1's, namely periods of at least 80% 1's and 20% or less 0's. I would like to create a new variable that flags each minute that is part of one of these dense periods of 1's. Also, I would like 5 consecutive zero's to indicate an automatic end of a period dense in 1's. I think this is likely a difficult programming issue and that it may not have a solution that is 100% accurate, but I am wondering if anyone has tried something like this in SAS before and has advice or can point me to any reading that might be helpful.

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