Glance at a(n) binDesign object

Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

# S3 method for binDesign
glance(x, ...)

Arguments

x

A binGroup::binDesign object.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

glance(), binGroup::binDesign()

Other bingroup tidiers: tidy.binDesign(), tidy.binWidth()

Value

A tibble::tibble() with exactly one row and columns:

power

Power achieved by the analysis.

n

Sample size uzed to achieve this power.

power.reached

Whether the desired power was reached.

maxit

Number of iterations performed.

Examples

library(binGroup)
des <- binDesign(nmax = 300, delta = 0.06,
                 p.hyp = 0.1, power = .8)

glance(des)
#> # A tibble: 1 x 4
#>   power     n power.reached maxit
#>   <dbl> <int> <lgl>         <int>
#> 1 0.805   240 TRUE            238
tidy(des)
#> # A tibble: 238 x 2
#>        n     power
#>    <int>     <dbl>
#>  1     3 0.000064 
#>  2     4 0.000248 
#>  3     5 0.000602 
#>  4     6 0.00117  
#>  5     7 0.0000813
#>  6     8 0.000157 
#>  7     9 0.000274 
#>  8    10 0.000443 
#>  9    11 0.000673 
#> 10    12 0.0000640
#> # … with 228 more rows

library(ggplot2)
ggplot(tidy(des), aes(n, power)) +
    geom_line()