Hello all, I have run a series of PROC MIXED models with a mix of categorical, continuous, and spline predictors plus multiple interaction terms on a sample of N=205 who each contributed 7 repeated measures (example syntax below). In fixed effects, one of the interaction terms is significant, which I plotted using the outpred and PROC SGPLOT options (first example graph below). Now, one of my coauthors is wanting me to test simple slopes of the interaction. I tried using the store option and PROC PLM procedures, but I get slightly different results of the simple slopes in the 2nd example graph below (i.e., two are negative and only one is slightly positive) compared to the visual interpretation from the PROC SGPLOT (i.e., two clearly positive, one strongly negative). The only examples I am finding online have to do with a single interaction term, so I'm not sure how this is handled in a multivariate context and whether that could be to blame for the slight difference in interpretation. If so, which one might be "correct" and how do I modify my syntax? PROC MIXED data=work.import covtest noclprint method = ML;
class fab400_id partner(ref="Yes") TransitionSingle(ref="Continuance of Singlehood OR In a Relationship")
TransitionRelat(ref="Continuance of Relationship OR Being Single") GID_Male(ref="Cisgender") dem06_L2(ref="Cisgender");
model pss_sum = Age_T1 GID_male dem06_L2 visit partner Length2 TransitionSingle TransitionRelat Time mspss_total_personmean fobss_total_personmean
partner*fobss_total_personmean Length2*fobss_total_personmean TransitionSingle*fobss_total_personmean TransitionRelat*fobss_total_personmean
Time*fobss_total_personmean /solution outpred=mixedout;
random intercept visit / sub=fab400_id type=vc;
Repeated / Subject=fab400_id;
store pred1;
run;
proc plm restore=pred1;
estimate 'Length slope, FOBS=mean-sd' Length2 1 Length2*fobss_total_personmean 1.41,
'Length slope, FOBS=mean' Length2 1 Length2*fobss_total_personmean 2.65,
'Length slope, FOBS=mean+sd' Length2 1 Length2*fobss_total_personmean 3.89 / e;
effectplot fit (x=Length2) / at(fobss_total_personmean=1.41 2.65 3.89);
run;
Data mixedout_nomiss;
set mixedout(where=(FOBS ne .));
run;
proc sgplot data=mixedout_nomiss nowall noborder;
styleattrs backcolor=white;
reg x=Length2 y=pred / nomarkers group=FOBS;
yaxis label="Stress";
xaxis integer label="Current Singlehood Length" ;
legenditem type=line name="-1" / label="Low FOBS" LINEATTRS=(pattern=solid color=darkblue thickness=2);
legenditem type=line name="0" / label="Average FOBS" LINEATTRS=(pattern=mediumdash color=firebrick thickness=2);
legenditem type=line name="1" / label="High FOBS" LINEATTRS=(pattern=longdashshortdash color=cadetblue thickness=2);
keylegend "-1" "0" "1" / noborder location=outside position=right
title="Fear of Being Single";
run; Thank you all in advance!
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