Before you run closure_generate(), you may want to get a
sense of the time it will take to run. Use closure_gauge_complexity() to
compute a heuristics-based complexity score. For reference, here is how it
determines the messages in closure_generate():
| Value | Message |
| if < 1 | (no message) |
| else if < 2 | "Just a second..." |
| else if < 3 | "This could take a minute..." |
| else | "NOTE: Long runtime ahead!" |
Arguments
- mean
String (length 1). Reported mean.
- sd
String (length 1). Reported sample standard deviation.
- n
Numeric (length 1). Reported sample size.
- scale_min, scale_max
Numeric (length 1 each). Minimal and maximal possible values of the measurement scale. For example, with a 1-7 Likert scale, use
scale_min = 1andscale_max = 7. Prefer the empirical min and max if available: they constrain the possible values further.
Details
The result of this function is hard to interpret. All it can do is to convey an idea about the likely runtime of CLOSURE. This is because the input parameters interact in highly dynamic ways, which makes prediction difficult.
In addition, even progress bars or updates at regular intervals (e.g., "10% complete") prove to be extremely challenging: the Rust code computes CLOSURE results in parallel, which makes it hard to get an overview of the total progress across all cores; and especially to display such information on the R level.
Examples
# Low SD, N, and scale range:
closure_gauge_complexity(
mean = 2.55,
sd = 0.85,
n = 84,
scale_min = 1,
scale_max = 5
)
#> [1] 0.3521825
# Somewhat higher:
closure_gauge_complexity(
mean = 4.26,
sd = 1.58,
n = 100,
scale_min = 1,
scale_max = 7
)
#> [1] 2.477121
# Very high:
closure_gauge_complexity(
mean = 3.81,
sd = 3.09,
n = 156,
scale_min = 1,
scale_max = 7
)
#> [1] 3.39794