Abstract: Random effects models are widely used to describe the characteristics of repeating measurement data. It is very necessary to make diagnosis for the models. In this paper, the influence curvature had been used to study perturbation diagnostics of autocorrelation coefficients in nonlinear models with AR( 1 ) errors. And the orange tree data was used to illustrate the results.