The Actual by Predicted plot appears by default. It provides a visual assessment of model fit that reflects variation due to random effects. It plots the observed values of Y against the marginal predicted values of Y. These are the predicted values obtained if you select Save Columns > Prediction Formula.
Denote the linear mixed model by E[Yγ] = Xβ + Zγ. Here is the vector of fixed effect coefficients and γ is the vector of random effect coefficients. The marginal predictions are the predictions from the fixed effects part of the predictive model, given by .
Denote the linear mixed model by E[Yγ] = Xβ + Zγ. Here is the vector of fixed effect coefficients and γ is the vector of random effect coefficients. The marginal residuals are the residuals from the fixed effects part of the predictive model:
Shows the residuals plotted against the predicted values of Y. You typically want to see the residual values scattered randomly about zero.
Denote the linear mixed model by E[Yγ] = Xβ + Zγ. Here is the vector of fixed effect coefficients and γ is the vector of random effect coefficients. The marginal predictions are the predictions from the fixed effects part of the predictive model, given by .
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Options that are appropriate for the model that you are fitting are enabled. See Marginal Profiler Plot for Treatment A for an example of a profiler. See Surface Profiler Showing the Response Surface for MODULUS and Silica = 1.2 for an example of a Surface Profiler. For more details, see the Profilers book.