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.

The initial Variogram report shows a plot of the empirical semivariance against distance. For additional background and details, see Spatial and Temporal Variability.

The nugget is the vertical jump from the value of 0 at the origin of the variogram to the value of the semivariance at a very small separation distance. A variogram model with a nugget has a discontinuity at the origin. The value of the theoretical curve for distances just above 0 is the nugget.