Performance analysis of covariance. See [rbmi_ancova()] for full details.
Usage
rbmi_ancova_single(
data,
outcome,
group,
covariates,
weights = c("counterfactual", "equal", "proportional_em", "proportional")
)Arguments
- data
A `data.frame` containing the data to be used in the model.
- outcome
string, the name of the outcome variable in `data`.
- group
string, the name of the group variable in `data`.
- covariates
character vector containing the name of any additional covariates to be included in the model as well as any interaction terms.
- 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.
Details
- `group` must be a factor variable with only 2 levels. - `outcome` must be a continuous numeric variable.
Examples
iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ]
iris2$Species <- factor(iris2$Species)
rbmi_ancova_single(iris2, "Sepal.Length", "Species", c("Petal.Length * Petal.Width"))
#> $var
#> $var$est
#> [1] 0.1128236
#>
#> $var$se
#> [1] 0.01637017
#>
#> $var$df
#> [1] 95
#>
#>
#> $trt_virginica
#> $trt_virginica$est
#> Speciesvirginica
#> -0.5010775
#>
#> $trt_virginica$se
#> [1] 0.1271019
#>
#> $trt_virginica$df
#> [1] 95
#>
#>
#> $lsm_versicolor
#> $lsm_versicolor$est
#> [1] 6.512539
#>
#> $lsm_versicolor$se
#> [1] 0.07188155
#>
#> $lsm_versicolor$df
#> [1] 95
#>
#>
#> $lsm_virginica
#> $lsm_virginica$est
#> [1] 6.011461
#>
#> $lsm_virginica$se
#> [1] 0.07188155
#>
#> $lsm_virginica$df
#> [1] 95
#>
#>