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Implements an Analysis of Covariance (ANCOVA)

Source: R/ancova_rbmi.R
rbmi_ancova_single.Rd

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.

Value

a list containing var with variance estimates as well as trt_* and lsm_* entries. See rbmi_ancova() for full details.

Details

  • group must be a factor variable with only 2 levels.

  • outcome must be a continuous numeric variable.

See also

rbmi_ancova()

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
#> 
#>