Combination of tern::s_summary, and ANCOVA based estimates for mean and diff between columns, based on ANCOVA function `s_ancova_j`
Usage
a_summarize_ancova_j(
df,
.var,
.df_row,
ref_path,
.spl_context,
...,
.stats = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
s_summarize_ancova_j(df, .var, .df_row, .ref_group, .in_ref_col, ...)Arguments
- df
: need to check on how to inherit params from tern::s_ancova
- .var
(
string)
single variable name that is passed byrtableswhen requested by a statistics function.- .df_row
(
data.frame)
data set that includes all the variables that are called in.varandvariables.- ref_path
(`character`)
path to the reference group.- .spl_context
(`environment`)
split context environment.- ...
Additional arguments passed to `s_ancova_j`.
- .stats
(`character`)
statistics to calculate.- .formats
(`list`)
formats for the statistics.- .labels
(`list`)
labels for the statistics.- .indent_mods
(`list`)
indentation modifications for the statistics.- .ref_group
(
data.frameorvector)
the data corresponding to the reference group.- .in_ref_col
(
flag)TRUEwhen working with the reference level,FALSEotherwise.
Value
* `a_summarize_ancova_j()` returns the corresponding list with formatted [rtables::CellValue()].
returns the statistics from tern::s_summary(x), appended with a new statistics based upon ANCOVA
Details
Combination of tern::s_summary, and ANCOVA based estimates for mean and diff between columns, based on ANCOVA function `s_ancova_j`
Functions
a_summarize_ancova_j(): Formatted analysis function which is used as `afun`. Note that the junco specific `ref_path` and `.spl_context` arguments are used for reference column information.
See also
Other Inclusion of ANCOVA Functions:
a_summarize_aval_chg_diff_j(),
s_ancova_j()
Examples
basic_table() |>
split_cols_by("Species") |>
add_colcounts() |>
analyze(
vars = "Petal.Length",
afun = a_summarize_ancova_j,
show_labels = "hidden",
na_str = tern::default_na_str(),
table_names = "unadj",
var_labels = "Unadjusted comparison",
extra_args = list(
variables = list(arm = "Species", covariates = NULL),
conf_level = 0.95,
.labels = c(lsmean = "Mean", lsmean_diff = "Difference in Means"),
ref_path = c("Species", "setosa")
)
) |>
analyze(
vars = "Petal.Length",
afun = a_summarize_ancova_j,
show_labels = "hidden",
na_str = tern::default_na_str(),
table_names = "adj",
var_labels = "Adjusted comparison (covariates: Sepal.Length and Sepal.Width)",
extra_args = list(
variables = list(
arm = "Species",
covariates = c("Sepal.Length", "Sepal.Width")
),
conf_level = 0.95,
ref_path = c("Species", "setosa")
)
) |>
build_table(iris)
#> setosa versicolor virginica
#> (N=50) (N=50) (N=50)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> n 50 50 50
#> Mean (SD) 1.46 (0.174) 4.26 (0.470) 5.55 (0.552)
#> Median 1.50 4.35 5.55
#> Min, max 1.00, 1.90 3.00, 5.10 4.50, 6.90
#> 25% and 75%-ile 1.40, 1.60 4.00, 4.60 5.10, 5.90
#> Adjusted Mean (SE) 1.46 (0.06) 4.26 (0.06) 5.55 (0.06)
#> Adjusted Mean (95% CI) 1.46 (1.34, 1.58) 4.26 (4.14, 4.38) 5.55 (5.43, 5.67)
#> Difference in Adjusted Means (95% CI) 2.80 (2.63, 2.97) 4.09 (3.92, 4.26)
#> p-value <0.001 <0.001
#> n 50 50 50
#> Mean (SD) 1.46 (0.174) 4.26 (0.470) 5.55 (0.552)
#> Median 1.50 4.35 5.55
#> Min, max 1.00, 1.90 3.00, 5.10 4.50, 6.90
#> 25% and 75%-ile 1.40, 1.60 4.00, 4.60 5.10, 5.90
#> Adjusted Mean (SE) 2.02 (0.08) 4.19 (0.05) 5.07 (0.06)
#> Adjusted Mean (95% CI) 2.02 (1.87, 2.17) 4.19 (4.09, 4.28) 5.07 (4.95, 5.18)
#> Difference in Adjusted Means (95% CI) 2.17 (1.96, 2.38) 3.05 (2.81, 3.29)
#> p-value <0.001 <0.001
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_summarize_ancova_j(
df,
.var = .var,
.df_row = .df_row,
variables = variables,
.ref_group = .ref_group,
.in_ref_col = FALSE,
conf_level = conf_level
)
#> $n
#> n
#> 50
#>
#> $sum
#> sum
#> 277.6
#>
#> $mean
#> mean
#> 5.552
#>
#> $sd
#> sd
#> 0.5518947
#>
#> $se
#> se
#> 0.0780497
#>
#> $mean_sd
#> mean sd
#> 5.5520000 0.5518947
#>
#> $mean_se
#> mean se
#> 5.5520000 0.0780497
#>
#> $mean_ci
#> mean_ci_lwr mean_ci_upr
#> 5.395153 5.708847
#> attr(,"label")
#> [1] "Mean 95% CI"
#>
#> $mean_sei
#> mean_sei_lwr mean_sei_upr
#> 5.47395 5.63005
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#>
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr
#> 5.000105 6.103895
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#>
#> $mean_ci_3d
#> mean mean_ci_lwr mean_ci_upr
#> 5.552000 5.395153 5.708847
#> attr(,"label")
#> [1] "Mean (95% CI)"
#>
#> $mean_pval
#> p_value
#> 4.093231e-51
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#>
#> $median
#> median
#> 5.55
#>
#> $mad
#> mad
#> 0
#>
#> $median_ci
#> median_ci_lwr median_ci_upr
#> 5.2 5.7
#> attr(,"conf_level")
#> [1] 0.9671609
#> attr(,"label")
#> [1] "Median 95% CI"
#>
#> $median_ci_3d
#> median median_ci_lwr median_ci_upr
#> 5.55 5.20 5.70
#> attr(,"label")
#> [1] "Median (95% CI)"
#>
#> $quantiles
#> quantile_0.25 quantile_0.75
#> 5.1 5.9
#> attr(,"label")
#> [1] "25% and 75%-ile"
#>
#> $iqr
#> iqr
#> 0.8
#>
#> $range
#> min max
#> 4.5 6.9
#>
#> $min
#> min
#> 4.5
#>
#> $max
#> max
#> 6.9
#>
#> $median_range
#> median min max
#> 5.55 4.50 6.90
#> attr(,"label")
#> [1] "Median (Min - Max)"
#>
#> $cv
#> cv
#> 9.940466
#>
#> $geom_mean
#> geom_mean
#> 5.525789
#>
#> $geom_sd
#> geom_sd
#> 1.102724
#>
#> $geom_mean_sd
#> geom_mean geom_sd
#> 5.525789 1.102724
#>
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr
#> 5.374343 5.681502
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#>
#> $geom_cv
#> geom_cv
#> 9.801743
#>
#> $geom_mean_ci_3d
#> geom_mean mean_ci_lwr mean_ci_upr
#> 5.525789 5.374343 5.681502
#> attr(,"label")
#> [1] "Geometric Mean (95% CI)"
#>
#> $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"
#>