A reference grid encapsulates everything needed to compute least-squares means, independently of the underlying model object. The "lsmobj" class is a minor extension of "ref.grid" where the linear predictors for the reference grid are transformed in some linear way such as marginal averages or contrasts.
Objects from the Classes
Objects of class "ref.grid" are most commonly created by calling the ref.grid function.
Objects of class "lsmobj" are created by calling lsmeans or a related function such as contrast.
Slots
model.info:
Object of class "list" containing the elements call (the call that produced the model), terms (its terms object), and xlev (factor-level information)
roles:
Object of class "list" containing at least the elements predictors, responses, and multresp. These are character vectors of names of these variables.
grid:
Object of class "data.frame" containing the combinations of the variables that define the reference grid. In addition, there is an auxiliary column named ".wgt." holding the observed frequencies or weights for each factor combination (excluding covariates). If the model has one or more offset() calls, there is an another auxiliary column named ".offset.". Auxiliary columns are not considered part of the reference grid. (However, any variables included in offset calls are in the reference grid.)
levels:
Object of class "list" with each entry containing the distinct levels of variables in the reference grid. Note that grid is obtained by applying the function expand.grid to this list
matlevs:
Object of class "list" Like levels but has the levels of any matrices in the original dataset. Matrix columns must always be reduced to a single value for purposes of the reference grid
linfct:
Object of class "matrix" giving the linear functions of the regression coefficients for predicting each element of the reference grid. The rows of this matrix go in one-to-one correspondence with the rows of grid, and the columns with elements of bhat
bhat:
Object of class "numeric" with the regression coefficients. If there is a multivariate response, this must be flattened to a single vector, and linfct and V redefined appropriately. Important: bhat must include any NA values produced by collinearity in the predictors. These are taken care of later in the estimability check.
nbasis:
Object of class "matrix" with the basis for the non-estimable functions of the regression coefficients. Every LS mean will correspond to a linear combination of rows of linfct, and that result must be orthogonal to all the columns of nbasis in order to be estimable. This will be NULL if everything is estimable
V:
Object of class "matrix", the symmetric variance-covariance matrix of bhat
dffun, dfargs:
Objects of class "function" and "list" respectively. dffun(k,dfargs) should return the degrees of freedom for the linear function sum(k*bhat), or NA if unavailable
misc:
A list containing additional information used by methods. These include at least the following: estName (the label for the estimates of linear functions), and the default values of infer, level, and adjust to be used in the summary method. Elements in this slot may be modified if desired using the update method.
post.beta:
A matrix containing a sample from the posterior distribution of the regression coefficients; or a 1 x 1 matrix of NA if this is not available. When it is non-trivial, the as.mcmc method returns post.beta times t(linfct), which is a sample from the posterior distribution of the LS means.
Extends
Class "lsmobj" extends Class "ref.grid", directly. There is hardly a difference between these classes except for how the slots linfct and grid are obtained, and their show methods.
Methods
All methods for these objects are S3 methods except for show.
show:
Prints the results of str for ref.grid objects, and summary for lsmobj objects.
str:
Displays a brief listing of the variables and levels defining the grid.
summary:
Displays a summary of estimates, standard errors, degrees of freedom, and optionally, tests and/or confidence intervals.
lsmeans:
Computes least-squares means and creates an "lsmobj" object.
confint:
Confidence intervals for lsmeans.
test:
Hypothesis tests.
cld:
Compact-letter display for tests of pairwise comparisons
contrast:
Contrasts among lsmeans.
pairs:
A special case of contrasts for pairwise comparisons.
update:
Change defaults used primarily by summary, such as transformation, p-value adjustment, and confidence level.
providing 
.