The Actual by Conditional Predicted plot appears by default. It provides a visual assessment of model fit that accounts for variation due to random effects. It plots the observed values of Y against the conditional predicted values of Y. These are the predicted values obtained if you select Save Columns > Conditional Prediction Formula.
Denote the linear mixed model by E[Y|γ] = Xβ + Zγ. Here is the vector of fixed effect coefficients and γ is the vector of random effect coefficients. The conditional predictions are the predictions obtained from the model given by .
Denote the linear mixed model by E[Y|γ] = Xβ + Zγ. Here is the vector of fixed effect coefficients and γ is the vector of random effect coefficients. The conditional residuals are given as follows:
Shows the conditional residuals plotted against the conditional predicted values of Y. You typically want to see the conditional residual scattered randomly about zero.
Denote the linear mixed model by E[Y|γ] = Xβ + Zγ. Here is the vector of fixed effect coefficients and γ is the vector of random effect coefficients. The conditional predictions are the predictions obtained from the model given by .
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 details about the profiler, see the Profilers book.