print.feasible: Print method for `feasible` objects

Description

Produces a concisey summary of a feasible intervention object. The printout summarizes information jointly over time and strategy, using tables with strategies as rows and time points as columns. Separate tables are printed for the proportion of infeasible mass (%infeasible) and the mean feasible value (Feasible).

Usage

# S3 method for feasible
print(x, digits = 3, strategies = "all", times = "all", ...)

Value

Invisibly returns x.

Arguments

x: A "feasible" object returned by feasible.
digits: Integer; number of digits used when printing numeric values.
strategies: Either "all" (default) or a numeric vector of strategy indices to include in the printed summary. When a numeric vector is supplied, all summaries and tables are restricted to these strategies.
times: Either "all" (default) or a numeric vector of time indices to include in the printed summary. When a numeric vector is supplied, all summaries and tables are restricted to these time points.
...: Ignored; provided for S3 method compatibility.

Details

The method extracts the data.frame stored in the "summary" attribute of x and optionally restricts it to the selected strategies and times. All reported values are based on this restricted data.

The summary data typically contains at least the following columns:

time: time index t.
Strategy: index of the intervention strategy.
Abar: target intervention value at time t.
%infeasible: proportion of mass (on the 0--1 scale) falling below the estimated density threshold for the targeted Abar.
Feasible: mean of the mapped feasible values (after replacing low-density bins) for the targeted bin.

The output consists of:

A short header showing how many strategies and time points exist in the underlying object, and how many are being displayed after subsetting via strategies and times.
Table 1: %infeasible summarized by strategy (rows) and time (columns), printed as percentage.
Table 2: Feasible (mean feasible value) summarized by strategy (rows) and time (columns), printed on the original scale.
A compact display of the Abar targets by strategy and time.

Within the selected subset, the method also checks whether each strategy uses the same Abar at every selected time point. If that is the case, the printout notes that each selected strategy corresponds to the same intervention pattern over time. Otherwise, differences in Abar across time are made visible by the Abar-by-time display.

Examples

Run this code

data(EFV)

# \donttest{
Lnodes <- c("adherence.1","weight.1",
            "adherence.2","weight.2",
            "adherence.3","weight.3",
            "adherence.4","weight.4")
Ynodes <- c("VL.0","VL.1","VL.2","VL.3","VL.4")
Anodes <- c("efv.0","efv.1","efv.2","efv.3","efv.4")

## ------------------------------------------------------------------
## Example 1: Static grid of targets (vector abar)
## Each strategy uses the same target value at every time point
## ------------------------------------------------------------------

abar_static <- seq(0, 10, by = 2)

set.seed(123)
m_static <- feasible(X = EFV,
                     Anodes = Anodes,
                     Lnodes = Lnodes,
                     Ynodes = Ynodes,
                     d.method = "parametric",
                     abar = abar_static,
                     grid.size = NULL,
                     left.boundary = 0,
                     right.boundary = 10)

## Full time x strategy summary
print(m_static)

## Use fewer digits in the numeric summaries
print(m_static, digits = 2)

## Focus on a subset of strategies (e.g., 1 and 3)
print(m_static, strategies = c(1, 3))

## Focus on early time points only (e.g., times 1, 2)
print(m_static, times = c(1, 2))

## Combine selection: only strategies 1 and 3 over times 1, 2, 3
print(m_static, strategies = c(1, 3), times = 1:3)

## ------------------------------------------------------------------
## Example 2: Non-constant intervention strategies (matrix abar)
## Strategies can have different target values at different time points
## ------------------------------------------------------------------

## Rows define strategies, columns define time points.
## The first row increases over time, the second decreases, the third increases.
abar_matrix <- rbind(
  c(0, 2, 4, 6, 8),  # strategy 1
  c(9, 6, 2, 1, 0),  # strategy 2
  c(1, 3, 5, 7, 9)   # strategy 3
)

set.seed(456)
m_matrix <- feasible(X = EFV,
                     Anodes = Anodes,
                     Lnodes = Lnodes,
                     Ynodes = Ynodes,
                     d.method = "parametric",
                     abar = abar_matrix,
                     grid.size = 1,
                     left.boundary = 0,
                     right.boundary = 10)

## Time x strategy summary where targets vary over time within strategies
print(m_matrix)

## Focus on strategies 1 and 3 over a subset of time points
print(m_matrix, strategies = c(1, 3), times = 1:3)
# }