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Tabulation of RBMI Results

Source: R/tabulate_rbmi.R
tabulate_rbmi.Rd

[Stable]

These functions can be used to produce tables from RBMI.

Usage

h_tidy_pool(x, visit_name, group_names)

s_rbmi_lsmeans(df, .in_ref_col, show_relative = c("reduction", "increase"))

a_rbmi_lsmeans(
  df,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

x

(list)
is a list of pooled object from rbmi analysis results. This list includes analysis results, confidence level, hypothesis testing type.

visit_name

(string)
single visit level.

group_names

(character)
group levels.

df

(data.frame)
input with LS means results.

.in_ref_col

(flag)
whether reference column is specified.

show_relative

(string)
'reduction' if (control - treatment, default) or 'increase' (treatment - control) of relative change from baseline?

ref_path

(character)
global reference group specification, see get_ref_info().

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

...

additional arguments for the lower level functions.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the 'auto' setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Value

The data.frame with results of pooled analysis for a single visit.

A list of statistics extracted from a tidied LS means data frame.

Functions

  • h_tidy_pool(): Helper function to produce data frame with results of pool for a single visit.

  • s_rbmi_lsmeans(): Statistics function which is extracting estimates from a tidied RBMI results data frame.

  • a_rbmi_lsmeans(): Formatted Analysis function which is used as afun.

Note

These functions have been forked from tern.rbmi. Additional features are:

  • Additional ref_path argument.

  • Extraction of variance statistics in the tidy() method.

  • Adapted to rbmi forked functions update with more than two treatment groups.