In　this　article,　we　consider　statistical　inference　for　longitudinal　partial　linear　models　when　the　response　variable　is　sometimes　missing　with　missingness　probability　depending　on　the　covariate　that　is　measured　with　error.　A　generalized　empirical　likelihood　(GEL)　method　is　proposed　by　combining　correction　attenuation　and　quadratic　inference　functions.　The　method　that　takes　into　consideration　the　correlation　within　groups　is　used　to　estimate　the　regression　coefficients.　Furthermore,　residual-adjusted　empirical　likelihood　(EL)　is　employed　for　estimating　the　baseline　function　so　that　undersmoothing　is　avoided.　The　empirical　log-likelihood　ratios　are　proven　to　be　asymptotically　Chi-squared,　and　the　corresponding　confidence　regions　for　the　parameters　of　interest　are　then　constructed.　Compared　with　methods　based　on　NAs,　the　GEL　does　not　require　consistent　estimators　for　the　asymptotic　variance　and　bias.　The　numerical　study　is　conducted　to　compare　the　performance　of　the　EL　and　the　normal　approximation-based　method,　and　a　real　example　is　analysed.