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!
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