An object returned by the sme function, inheriting from class
sme, representing a fitted smoothing-splines mixed-effects model
An sme object must contain the following components.
a list containing an image of the sme call that produced the object.
an n+1 by p matrix where n is the number of subjects and p is the number of knots used in the spline basis. The first row of the matrix corresponds to the fitted values of the mean curve at the knots, and the remaining n rows correspond to the fitted values of the individual deviations from the mean curve at the knots.
a vector of model fitted values corresponding to the original observations.
the log-likelihood of the fitted model.
a vector of model residuals.
the number of observations.
a data frame of the original data used to fit the model with variables y,
tme and ind corresponding to observations, time points and subject identifiers
respectively.
a vector consisting of named components mu and v corresponding to the
degrees of freedom of the mean and subject curves respectively.
a vector of named components mu and v corresponding to
the smoothing parameters for the mean and subject curves respectively.
a list with named components sigmaSquared and D corresponding to
the error variance and (unregularized) variance of the random-effects respectively.
the number of iterations of the EM algorithm that ran before convergence (or the limit was reached)
a numeric code indicating diagnostic information from the EM algorithm with zero indicating a successful run.
a vector of the knots used in the spline basis when they do not coincide with the unique design time points.