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Junco Extended ANCOVA Function

Source: R/summarize_ancova.R
s_ancova_j.Rd

Extension to tern:::s_ancova, 3 extra statistics are returned * `lsmean_se`: Marginal mean and estimated SE in the group. * `lsmean_ci`: Marginal mean and associated confidence interval in the group. * `lsmean_diffci`: Difference in mean and associated confidence level in one combined statistic. In addition, the LS mean weights can be specified. In addition, also a NULL .ref_group can be specified, the lsmean_diff related estimates will be returned as NA.

Usage

s_ancova_j(
  df,
  .var,
  .df_row,
  variables,
  .ref_group,
  .in_ref_col,
  conf_level,
  interaction_y = FALSE,
  interaction_item = NULL,
  weights_emmeans = "counterfactual"
)

Arguments

df

: need to check on how to inherit params from tern::s_ancova

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

.df_row

(data.frame)
data set that includes all the variables that are called in .var and variables.

variables

(named list of string)
list of additional analysis variables, with expected elements:

  • arm (string)
    group variable, for which the covariate adjusted means of multiple groups will be summarized. Specifically, the first level of arm variable is taken as the reference group.

  • covariates (character)
    a vector that can contain single variable names (such as "X1"), and/or interaction terms indicated by "X1 * X2".

.ref_group

(data.frame or vector)
the data corresponding to the reference group.

.in_ref_col

(flag)
TRUE when working with the reference level, FALSE otherwise.

conf_level

(proportion)
confidence level of the interval.

interaction_y

(string or flag)
a selected item inside of the interaction_item variable which will be used to select the specific ANCOVA results. if the interaction is not needed, the default option is FALSE.

interaction_item

(string or NULL)
name of the variable that should have interactions with arm. if the interaction is not needed, the default option is NULL.

weights_emmeans

(`string`)
argument from [emmeans::emmeans()], `"counterfactual"` by default.

Value

returns a named list of 8 statistics (3 extra compared to `tern:::s_ancova()`).

See also

Other Inclusion of ANCOVA Functions: a_summarize_ancova_j(), a_summarize_aval_chg_diff_j()

Examples

library(dplyr)
library(tern)

df <- iris |> filter(Species == "virginica")
.df_row <- iris
.var <- "Petal.Length"
variables <- list(arm = "Species", covariates = "Sepal.Length * Sepal.Width")
.ref_group <- iris |> filter(Species == "setosa")
conf_level <- 0.95
s_ancova_j(df, .var, .df_row, variables, .ref_group, .in_ref_col = FALSE, conf_level)
#> $n
#> [1] 50
#> attr(,"label")
#> [1] "n"
#> 
#> $lsmean
#> [1] 5.071002
#> attr(,"label")
#> [1] "Adjusted Mean"
#> 
#> $lsmean_se
#> [1] 5.07100244 0.06041213
#> attr(,"label")
#> [1] "Adjusted Mean (SE)"
#> 
#> $lsmean_ci
#> [1] 5.071002 4.951593 5.190412
#> attr(,"label")
#> [1] "Adjusted Mean (95% CI)"
#> 
#> $lsmean_diff
#> [1] 3.062603
#> attr(,"label")
#> [1] "Difference in Adjusted Means"
#> 
#> $lsmean_diff_ci
#> [1] 2.808526 3.316680
#> attr(,"label")
#> [1] "Difference in Adjusted Means 95% CI"
#> 
#> $lsmean_diffci
#> [1] 3.062603 2.808526 3.316680
#> attr(,"label")
#> [1] "Difference in Adjusted Means (95% CI)"
#> 
#> $pval
#> [1] 8.117283e-52
#> attr(,"label")
#> [1] "p-value"
#>