This class of objects is returned from the lme() function to represent a fitted linear mixed effects model. Objects of this class have methods for the generic functions anova(), coef(), fitted(), fixed.effects(), plot(), predict(), print(), random.effects(), residuals(), and summary().

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VALUE:

The following components must be included in a legitimate lme object. The residuals, fitted values and coefficients can be extracted by the generic functions of the same name, or by the "$" operator.

coefficients:

a list with two components, fixed and random, where the first is a vector containing the estimated coefficients for the fixed effects - the names of the coefficients are the same as those in the fixed formula of the call to lme(), and the second is a matrix containing the estimated coefficients for the random effects. The columns refer to the parameters in the random formula, and the rows to the cluster levels; the names of the coefficients are the same as in the random formula (columns) and the cluster levels (rows).

fitted.values:

a data frame containing the population and cluster fitted values. The population fitted values are evaluated at the converged estimates of the fixed effects and the mean of the random effects (that is the random effects are set to zero). The cluster fitted values are evaluated at the converged estimates of the fixed effects and the conditional estimates of the random effects.

residuals:

a data frame containing the population and cluster residuals from the fit. The population residuals are the observed values minus the population fitted values and the cluster residuals are the observed values minus the cluster fitted values. If the variance function structure used in the fit is different than "identity", a column with the estimated individual standard deviations will be included in the residuals data frame.

var.ran:

Random effects variance-covariance-correlation matrix estimate. The variances estimates for the random effects are displayed on the main diagonal, covariances above the diagonal and correlations below the diagonal.

var.fix:

the estimated variance-covariance matrix of the fixed effects.

sigma:

estimate of the cluster residual standard deviation.

call:

a list containing an image of the lme() call.

cluster:

a vector containing the clusters levels.

nobs:

nclus:

the number of observations (nobs) and clusters (nclus) used in the fit.

loglik:

the loglikelihood, or restricted loglikelihood, value at convergence.

est.method:

estimation method used in the fit, either "ML" or "RML".

fixed,random:

formulas representing the fixed and random components of the model.

niter:

number of iterations used in iterative algorithm.

re.block:

re.structure:

the blocking and covariance structures used for the random effects.

re.paramtr:

the parametrization used for the unstructured variance-covariance matrices of the blocks of random effects.

sizetheta:

length of the parameter vector theta used to obtain the scaled variance-covariance matrix of the random effects D.

serial.structure:

the variance-covariance structure assumed for the within-cluster errors.

alpha:

the estimated serial structure parameter vector, if serial.covariate is not equal to "identity".

var.function:

the variance function structure used for the within-cluster errors.

delta:

the estimated variance function parameters, if var.function is not equal to "identity".

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SEE ALSO:

lme , lme.formula , lme.lmList .