We　study　model　selection　and　model　averaging　in　varying-coefficient　partially　linear　model　with　longitudinal　data.　The　model　is　estimated　by　combining　spline　approximations　and　the　generalized　estimating　equations.　The　corresponding　linear　coefficient　estimators　are　shown　to　be　asymptotically　normal.　Then　we　propose　a　focused　information　criterion　and　a　frequentist　model　average　estimator,　and　establish　the　asymptotic　properties　of　the　proposed　estimators.　Simulation　studies　show　the　proposed　methods　are　superior　to　the　existing　ones　even　if　the　covariance　structure　is　misspecified.　The　procedures　are　further　applied　to　a　real　data　case.