an approximate covariance matrix for the variance-covariance coefficients. If apVar = FALSE in the control values used in the call to lme, this component is NULL.

a list containing an image of the lme call that produced the object.

a list with two components, fixed and random, where the first is a vector containing the estimated fixed effects and the second is a list of matrices with the estimated random effects for each level of grouping. For each matrix in the random list, the columns refer to the random effects and the rows to the groups.

a list with the contrasts used to represent factors in the fixed effects formula and/or random effects formula. This information is important for making predictions from a new data frame in which not all levels of the original factors are observed. If no factors are used in the lme model, this component will be an empty list.

a list with basic dimensions used in the lme fit, including the components N - the number of observations in the data, Q - the number of grouping levels, qvec - the number of random effects at each level from innermost to outermost (last two values are equal to zero and correspond to the fixed effects and the response), ngrps - the number of groups at each level from innermost to outermost (last two values are one and correspond to the fixed effects and the response), and ncol - the number of columns in the model matrix for each level of grouping from innermost to outermost (last two values are equal to the number of fixed effects and one).

a data frame with the fitted values as columns. The leftmost column corresponds to the population fixed effects (corresponding to the fixed effects only) and successive columns from left to right correspond to increasing levels of grouping.

a list with components X and terms specifying the denominator degrees of freedom for, respectively, t-tests for the individual fixed effects and F-tests for the fixed-effects terms in the models.

a data frame with the grouping factors as columns. The grouping level increases from left to right.

the (restricted) log-likelihood at convergence.

the estimation method: either "ML" for maximum likelihood, or "REML" for restricted maximum likelihood.

an object inheriting from class lmeStruct, representing a list of mixed-effects model components, such as reStruct, corStruct, and varFunc objects.

the number of iterations used in the iterative algorithm.

a data frame with the residuals as columns. The leftmost column corresponds to the population residuals and successive columns from left to right correspond to increasing levels of grouping.

the estimated within-group error standard deviation.

an approximate covariance matrix of the fixed effects estimates.