lsmeans: Least Square Means

In rbmi: Reference Based Multiple Imputation

Least Square Means

Description

Estimates the least square means from a linear model. The exact implementation / interpretation depends on the weighting scheme; see the weighting section for more information.

Usage

lsmeans(
  model,
  ...,
  .weights = c("counterfactual", "equal", "proportional_em", "proportional")
)

Arguments

model	A model created by lm.
...	Fixes specific variables to specific values i.e. trt = 1 or age = 50. The name of the argument must be the name of the variable within the dataset.
.weights	Character, either "counterfactual" (default), "equal", "proportional_em" or "proportional". Specifies the weighting strategy to be used when calculating the lsmeans. See the weighting section for more details.

Weighting

Counterfactual

For weights = "counterfactual" (the default) the lsmeans are obtained by taking the average of the predicted values for each patient after assigning all patients to each arm in turn. This approach is equivalent to standardization or g-computation. In comparison to emmeans this approach is equivalent to:

emmeans::emmeans(model, specs = "<treatment>", counterfactual = "<treatment>")

Note that to ensure backwards compatibility with previous versions of rbmi weights = "proportional" is an alias for weights = "counterfactual". To get results consistent with emmeans's weights = "proportional" please use weights = "proportional_em".

Equal

For weights = "equal" the lsmeans are obtained by taking the model fitted value of a hypothetical patient whose covariates are defined as follows:

• Continuous covariates are set to mean(X)

• Dummy categorical variables are set to 1/N where N is the number of levels

• Continuous * continuous interactions are set to mean(X) * mean(Y)

• Continuous * categorical interactions are set to mean(X) * 1/N

• Dummy categorical * categorical interactions are set to 1/N * 1/M

In comparison to emmeans this approach is equivalent to:

emmeans::emmeans(model, specs = "<treatment>", weights = "equal")

Proportional

For weights = "proportional_em" the lsmeans are obtained as per weights = "equal" except instead of weighting each observation equally they are weighted by the proportion in which the given combination of categorical values occurred in the data. In comparison to emmeans this approach is equivalent to:

emmeans::emmeans(model, specs = "<treatment>", weights = "proportional")

Note that this is not to be confused with weights = "proportional" which is an alias for weights = "counterfactual".

Fixing

Regardless of the weighting scheme any named arguments passed via ... will fix the value of the covariate to the specified value. For example, lsmeans(model, trt = "A") will fix the dummy variable trtA to 1 for all patients (real or hypothetical) when calculating the lsmeans.

See the references for similar implementations as done in SAS and in R via the emmeans package.

References

https://CRAN.R-project.org/package=emmeans

https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_glm_details41.htm

Examples

## Not run: 
mod <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lsmeans(mod)
lsmeans(mod, Species = "virginica")
lsmeans(mod, Species = "versicolor")
lsmeans(mod, Species = "versicolor", Petal.Length = 1)

## End(Not run)

rbmi documentation built on Oct. 16, 2024, 5:09 p.m.