In　this　paper,　we　discuss　the　model　checking　problem　for　a　partial　linear　model　when　some　covariates　are　missing　at　random.　A　weighted　model-adjustment　method　is　applied　to　estimate　the　regression　coefficients　and　the　nonparametric　function　for　the　null　hypothetical　partial　linear　model.　A　testing　procedure　based　on　a　residual-marked　empirical　process　is　developed　to　check　the　adequacy　of　the　partial　linear　model.　It　is　shown　that　the　proposed　test　is　consistent　and　can　detect　the　local　alternatives　converging　to　the　null　hypothetical　model　at　the　rate　n(-1/2).　Since　the　asymptotic　null　distribution　of　the　testing　statistics　is　case-dependent,　an　adjusted　wild　bootstrap　method　is　used　to　decide　the　critical　value,　which　is　proved　to　be　consistent.　A　simulation　study　and　a　real　data　analysis　are　conducted　to　show　that　the　proposed　procedure　works　well.