Your ESTIMATE statement in PROC PLM (if they are estimable) tests if the slopes for time at low, medium and high resilience are jointly zero. This is not testing if the slopes are different among different levels of resilience, which is what the interaction term tests. ... View more

PROC LOGISTIC is for dichotomous response variables by default. If A is a continuous variable, you might want to use PROC REG. proc reg data=Dataset; Model a= b c; Run; Quit; ... View more

  You treated WEEK as a continuous variable. Then when you say weeks 1-3, do you mean the average for week 1 to week 3? That is, the average value for WEEK is 2? Similarly, weeks 4-6 means the average value for WEEK would be 5? If so, you might see if the following ESTIMATE statement does what you want: proc mixed data=data; class id group(ref=first) ; model y=week group group*week; random intercept week /sub=id type=un ; estimate "difference between weeks 1-3 and 4-6 for group 2 versus difference between weeks 1-3 and 4-6 for group 1' group*week -3 3; run; ... View more

Like @PaigeMiller pointed out, you can use the OUTPUT statement to get what you wanted. But I would use the residuals rather than the predicted values to get the observations that are used in the analysis. This is because if the response variable is missing and none of the independent variables is missing, then this observation will not be used in the analysis but you would still get the predicted value for this observation. If you use the residuals, (output out=out r=residuals;)  you would correctly identify those observations included in the analysis (residuals not missing). ... View more

The variable tx contains the transformed x values. The documentation below describes the spline basis that is used to compute the transformed x -- https://go.documentation.sas.com/?cdcId=pgmsascdc&cdcVersion=v_009&docsetId=statug&docsetTarget=statug_transreg_details24.htm&locale=en   The following sample program illustrates how the predicted values for a spline model in PROC TRANSREG are computed, including how the transformed Tx values are computed. It is a function of the spline basis.   data test;    do x = 1 to 10;       y = x + log(x) + 2 * sin(x) + 10 * normal(7);       output;    end; run;   **transreg doesn't output the bspline... save coefficients here   for bspline basis output in next transreg run with ODS; **save regression coefficients using output statement;   title2 'Fit Curve, Get Predicted Values for All Observations'; proc transreg data=test detail ;    model ide(y) = spline(x / degree=3) / detail nomiss;    output out=spline p mrc;    ods output details=sdetails;    run;   proc print data=spline;   title 'TRANSREG OUT= data set using SPLINE transformation'; run;   proc print data=sdetails;   title 'TRANSREG DETAILS= data set using SPLINE transformation'; run;   *compare regression with transreg after transformation on x; *note that parameters here agree with last obs of out=spline data set; proc reg data=spline outest=outest;    model y=tx;    title 'REG output for Y vs. Transformed X variable'; quit;   *get the regression coefficients (only) from transreg run; data regcoeff;    set spline(where=(upcase(_name_)='TY'));    keep tintercept tx;    rename tintercept=regb0 tx=regb1; run;   proc print data=regcoeff;   title 'TRANSREG coefficients using SPLINE transformation'; run;   *** so the model is Py = regb0 + regb1*tx =  -1.98554  +  1.54593*tx; *** Tx is the transformed variable that comes from the spline basis, which is illustrated next;   *** the following program illustrates how tx is computed;   /*-------------------get TX manually-----------------------*/ proc transreg data=x; /* get the B-spline basis */    model ide(y) = bspline(x) / detail nomiss;    output out=manually; run;   proc print data=manually;   title 'TRANSREG OUT= data set using BSPLINE transformation'; run;   *get transformation coefficients and restructure data set; data getcoeff;    set sdetails(where=(term=2 & part>=9));    part=9-part;    keep numericvalue part; run;   proc print data=getcoeff; run;   proc transpose data=getcoeff out=splinecoef prefix=bx;    id part;    var numericvalue; run;   proc print data=regcoeff;   title 'Subset of SDETAILS output'; run;   proc print data=splinecoef; run;   data manually; /* Plug in the coefficients from DETAIL */    set manually;    if _n_=1 then set splinecoef;    if _n_=1 then set regcoeff;    tx =  bx0 * x_0 + bx_1*x_1 + bx_2* x_2 + bx_3*x_3 ;    predicty=regb0+regb1*tx; run;   **compare predicty with Py from out=spline; data compare;    merge spline(keep=Py _name_ where=(upcase(_name_)^='TY'))          manually(keep=predicty); run;   proc print data=compare;   title 'Compare resulting computations'; run;       ... View more