Print a Bias and Uncertainty Corrected Sample Size Result

Prints the aligned term/value table (the planned-study results followed by the rows echoing the planning inputs), then factual footer lines naming the design, for the regression planners the predictor frame (random_predictors), the unit the sample size is counted in, and the largest assurance the prior result can support. Whole numbers print clean; other values print at getOption("bucss.digits", 3) significant figures, except that a value below 1 is never shown as 1: it is given as many more figures as it takes to stay below 1 (a power of .99957 prints as 0.9996). Only the display is rounded; the stored values keep full precision.

Usage

# S3 method for class 'bucss_plan'
print(x, digits = getOption("bucss.digits", 3L), ...)

Arguments

x: An object of class bucss_plan.
digits: Significant figures for non-integer values; defaults to getOption("bucss.digits", 3).
...: Further arguments, ignored.

Value

x, invisibly.

Examples

result <- ss_buc_independent_t(t_observed = 3, n = c(50, 55),
  assurance = .90)
result
#>  term                  value
#>  necessary_n_per_group 1485 
#>  total_N               2970 
#>  actual_power          0.803
#>  ncp_adjusted          0.526
#>  smd_planning          0.103
#>  t_observed            3    
#>  n_1                   50   
#>  n_2                   55   
#>  alpha_prior           0.05 
#>  alpha_planned         0.05 
#>  assurance             0.9  
#>  desired_power         0.8  
#> 
#> Design: Independent t test
#> Sample size unit: per group
#> Largest supportable assurance: .93

# Display only: the stored values keep full precision.
print(result, digits = 6)
#>  term                  value   
#>  necessary_n_per_group 1485    
#>  total_N               2970    
#>  actual_power          0.802709
#>  ncp_adjusted          0.526243
#>  smd_planning          0.103205
#>  t_observed            3       
#>  n_1                   50      
#>  n_2                   55      
#>  alpha_prior           0.05    
#>  alpha_planned         0.05    
#>  assurance             0.9     
#>  desired_power         0.8     
#> 
#> Design: Independent t test
#> Sample size unit: per group
#> Largest supportable assurance: .93
result$value[result$term == "ncp_adjusted"]
#> [1] 0.5262426